Meme-based social abuse detection is challenging because harmful intent often relies on implicit cultural symbolism and subtle cross-modal incongruence. Prior approaches, from fusion-based methods to in-context learning with Large Vision-Language Models (LVLMs), have made progress but remain limited by three factors: i) cultural blindness (missing symbolic context), ii) boundary ambiguity (satire vs. abuse confusion), and iii) lack of interpretability (opaque model reasoning). We introduce CROSS-ALIGN+, a three-stage framework that systematically addresses these limitations: (1) Stage I mitigates cultural blindness by enriching multimodal representations with structured knowledge from ConceptNet, Wikidata, and Hatebase; (2) Stage II reduces boundary ambiguity through parameter-efficient LoRA adapters that sharpen decision boundaries; and (3) Stage III enhances interpretability by generating cascaded explanations. Extensive experiments on five benchmarks and eight LVLMs demonstrate that CROSS-ALIGN+ consistently outperforms state-of-the-art methods, achieving up to 17% relative F1 improvement while providing interpretable justifications for each decision.

Graph-level anomaly detection (GLAD) is a critical task to identify graphs with abnormal properties in various domains, ranging from fraudulent social networks to malicious botnets on online platforms. The dominant paradigm for existing GLAD detectors has been partially unsupervised, relying on training data composed exclusively of normal samples. However, this partially unsupervised paradigm inevitably requires a costly expert filtering process to ensure the training data is free of anomalies. This creates a significant gap between current approaches and the real-world necessity of a fully unsupervised paradigm, which involves training a model directly on real-world data ''as-is'', with its inherent mix of normal and anomalous samples. To bridge this gap, we incorporate uncertainty learning into GLAD to promote fully unsupervised learning. We propose two frameworks: Score Uncertainty Learning (SUL) and Graph-data Uncertainty Learning (GUL). Specifically, SUL enhances existing GLAD detectors by modeling uncertainty through Gaussian distributions over the detectors' predictions, adaptively attenuating the influence of potential anomalies. GUL is an end-to-end framework that iteratively optimizes anomaly detection and uncertainty modeling via an Expectation-Maximization algorithm. In addition, we develop a dedicated loss that utilizes potential anomalies to enhance the effectiveness and robustness of GUL. Empirical results on sixteen benchmark datasets, covering real-world graphs from social networks and online platforms, demonstrate the superiority of our methods and highlight the promise of incorporating uncertainty into fully unsupervised GLAD.

Social event detection (SED) involves identifying and analyzing significant real-world events using data generated on social media platforms. With the rapid growth of platforms like Weibo and Twitter, users are sharing not just text but also images. However, most existing SED methods remain text-focused, limiting their ability to fully capture the complexity of real-world social dynamics. Moreover, the lack of multi-modal datasets specifically designed for SED has blocked the development of models that can effectively exploit these rich content types. To address these limitations, we introduced WEIBO2022, an extensive multi-modal SED dataset that includes both text and image data. The dataset is available in two versions: WEIBO2022-Medium, containing 25,435 entries and WEIBO2022-Large, containing 79,825 entries. In addition, we presented a novel network called the Key Instance-driven, Quality-aware Graph Neural Network (KQGNN), which features a key instance-driven library, a quality-aware learning process, and a multi-modal fusion module, enhancing its ability to detect events accurately in both offline and real-time settings. Extensive experiments showcase the exceptional performance and superiority of the proposed model, showing improvements in detection accuracy and effective prevention of catastrophic forgetting during continuous training.

Brain graphs (brain connectivity networks) play an important role in modeling the complex structure of the human brain. Furthermore, brain graph learning based on graph neural networks (GNNs) has recently attracted growing interest. Although existing methods have made great progress in brain disorder prediction and pathogenic analysis, there are two key problems: (1) They rarely utilize the pathogenic reason of brain disorders, that is, salient brain regions always lead to abnormal connections between brain regions, to extract critical brain graph information for disorder analysis; (2) Since most of the available brain graph data is limited, how can we improve the performance of brain graph learning models on insufficient training data? Thus, in this paper, we learn brain graph representations for disorder prediction and analyze disorder-specific brain regions and connections from the subgraph perspective. Besides, we introduce the cross-domain brain graph learning framework to alleviate the problem of poor model performance on limited data. To consider the pathogenic reason by brain subgraphs, we first propose the node entropy of brain graphs based on brain graph properties to extract important nodes. We then introduce subgraph information bottleneck to refine the critical subgraph from the rough subgraph generated by these important nodes, recognizing important connections related to disorders. To achieve a better model performance on limited data, we design a cross-domain brain graph learning framework to improve the subgraph extraction model by the meta-learning method. The subgraph extraction model is pre-trained on a large source training dataset and then quickly adapted to target task dataset. Besides, a simple yet effective feature alignment module is applied to mitigate the negative transfer problem for cross-domain datasets. Extensive experimental results, including disorder prediction and pathogenic analysis on real-world neuroimaging data, demonstrate the effectiveness of our method.

Graph-level anomaly detection (GLAD) aims to identify graphs that significantly deviate from others in a graph dataset. Existing methods predominantly rely on standard Graph Neural Networks (GNNs) to learn graph representations, but they often overlook subgraph-level information, which provides essential structural and semantic cues for distinguishing normal and anomalous graphs. This limitation not only compromises the detection performance but also hinders the interpretability of GLAD predictions. To address these challenges, we propose NGLAD, a novel framework that introduces the concept of normality-relevant subgraphs that capture shared patterns across normal graphs. These subgraphs serve as key indicators to distinguish normal graphs from anomalies that that often lack or deviate from such patterns. During model training, by explicitly modeling the shared subgraph patterns inherent in normal graphs through a Subgraph Extractor and a Normality Learner, NGLAD identifies the subgraphs most relevant to normality. Leveraging the One-class Information Bottleneck principle, these modules ensure that the extracted subgraphs retain only the most informative features of normality while filtering out irrelevant nodes and edges. During inference, NGLAD detects anomalies by evaluating inconsistencies in representations between the input graph and its extracted subgraph. Extensive evaluations on synthetic and real-world datasets demonstrate that NGLAD significantly outperforms state-of-the-art methods in detection performance while offering interpretable explanations.

Graph Neural Networks (GNNs) have been widely used for learning representations of graph-structured data, achieving remarkable success in various graph-related Web applications, such as fraud detection. To generate node representations, GNN-based models operate message-passing mechanisms that aim to smooth the learned representations in a local neighborhood. However, fraudsters increasingly employ sophisticated “camouflage” tactics, exhibiting normal behaviors by strategically forming numerous connections with legitimate entities. As a result, existing GNN-based methods struggle to effectively tackle such fraudulent activities due to their reliance on homophily-based message-passing architectures. These methods fail to generate discriminative representations, which is crucial for distinguishing fraudsters from benign entities. To address this problem, we propose a novel Discriminative Enhanced Aggregation Graph Neural Network-based FraudDEtectioNMoDel (DEFEND) . DEFEND incorporates tailored discriminative mechanisms that strengthen representation learning at two complementary levels: (i) intra-relation and (ii) inter-relation. While prior approaches primarily focus on intra-relation patterns and overlook inter-relation information, DEFEND integrates both to capture subtle inconsistencies in fraudster behavior. Specifically, an edge discriminating mechanism classifies neighborhoods into homophily or heterophily-based views by leveraging node attributes and structural characteristics, and a camouflage-aware dual-channel aggregation module captures different frequencies of information tailored to these views to generate rich intra-relation node representations. While prior approaches typically rely on intra-relation information within each relation type, they overlook the discriminative signals that arise from correlations across different relations. In DEFEND, we observe that fraudsters often avoid forming consistent cross-relation interactions, whereas benign entities tend to establish them more frequently. This discrepancy creates a distinctive behavioral pattern. To capture this, we introduce an inter-relation correlation mechanism that correlates a node’s intra-relation representations across multiple relation types using an attention-based weighting scheme. By adaptively weighing the importance of each relation and integrating their contributions, DEFEND enhances the discriminative power of node representations. This mechanism enables the model to leverage both intra-relation and inter-relation levels of information, leading to richer and more robust representations for fraud detection. Finally, a multi-relation combination module aggregates information across different relation types, emphasizing the importance of node–relation pairs in the embedding. We conducted extensive experiments on two real-world fraud datasets to demonstrate the effectiveness of our proposed model, and our results show that DEFEND outperforms the state-of-the-art baselines. The source codes and datasets of our work are available at https://github.com/VenusHaghighi/DEFEND .

Social event detection involves identifying and categorizing important events from social media, which relies on labeled data, but annotation is costly and labor-intensive. To address this problem, we propose Augmentation framework for Social Event Detection (SED-Aug), a plug-and-play dual augmentation framework, which combines explicit text-based and implicit feature-space augmentation to enhance data diversity and model robustness. The explicit augmentation utilizes LLMs to enhance textual information through five diverse generation strategies. For implicit augmentation, we design five novel perturbation techniques that operate in the feature space on structural fused embeddings. These perturbations are crafted to keep the semantic and relational properties of the embeddings and make them more diverse. Specifically, SED-Aug outperforms the best baseline model by approximately 17.67% on the Twitter2012 dataset and by about 15.57% on the Twitter2018 dataset in terms of the average F1 score.

Social event detection (SED) is the task of identifying, categorizing, and tracking events from social data sources such as social media posts, news articles, and online discussions.Existing state-of-the-art (SOTA) SED models predominantly rely on graph neural networks (GNNs), which involve complex graph construction and time-consuming training processes, limiting their practicality in real-world scenarios.In this paper, we rethink the key challenge in SED: the informal expressions and abbreviations of short texts on social media platforms, which impact clustering accuracy.We propose a novel framework, LLM-enhanced Social Event Detection (LSED), which leverages the rich background knowledge of LLMs to address this challenge.Specifically, LSED utilizes LLMs to formalize and disambiguate short texts by completing abbreviations and summarizing informal expressions.Furthermore, we introduce hyperbolic space embeddings, which are more suitable for natural language sentence representations, to enhance clustering performance.Extensive experiments on two challenging realworld datasets demonstrate that LSED outperforms existing SOTA models, achieving improvements in effectiveness, efficiency, and stability.Our work highlights the potential of LLMs in SED and provides a practical solution for real-world applications.The code is available at GitHub 1 .

Given a piece of text, a video clip, and a reference audio, the movie dubbing task aims to generate speech that aligns with the video while cloning the desired voice. The existing methods have two primary deficiencies: (1) They struggle to simultaneously hold audio-visual sync and achieve clear pronunciation; (2) They lack the capacity to express user-defined emotions. To address these problems, we propose EmoDubber, an emotion-controllable dubbing architecture that allows users to specify emotion type and emotional intensity while satisfying high-quality lip sync and pronunciation. Specifically, we first design Lip-related Prosody Aligning (LPA), which focuses on learning the inherent consistency between lip motion and prosody variation by duration level contrastive learning to incorporate reasonable alignment. Then, we design Pronunciation Enhancing (PE) strategy to fuse the video-level phoneme sequences by efficient conformer to improve speech intelligibility. Next, the speaker identity adapting module decodes acoustics prior and inject the speaker style embedding. After that, the proposed Flow-based User Emotion Controlling (FUEC) is used to synthesize waveform by flow matching prediction network conditioned on acoustics prior. In this process, the FUEC determines the gradient direction and guidance scale based on the user’s emotion instructions by the positive and negative guidance mechanism, which focuses on amplifying the desired emotion while suppressing others. Extensive experimental results demonstrate favorable performance compared to several state-of-the-art methods. The code and trained models will be made available at https://github.com/GalaxyCong/DubFlow.

Generalized zero-shot learning aims to recognize both seen and unseen classes with the help of semantic information that is shared among different classes. It inevitably requires consistent visual-semantic alignment. Existing approaches fine-tune the visual backbone by seen-class data to obtain semantic-related visual features, which may cause overfitting on seen classes with a limited number of training images. This paper proposes a novel visual and semantic prompt collaboration framework, which utilizes prompt tuning techniques for efficient feature adaptation. Specifically, we design a visual prompt to integrate the visual information for discriminative feature learning and a semantic prompt to integrate the semantic formation for visual-semantic alignment. To achieve effective prompt information integration, we further design a weak prompt fusion mechanism for the shallow layers and a strong prompt fusion mechanism for the deep layers in the network. Through the collaboration of visual and semantic prompts, we can obtain discriminative semantic-related features for generalized zero-shot image recognition. Extensive experiments demonstrate that our framework consistently achieves favorable performance in both conventional zero-shot learning and generalized zero-shot learning benchmarks compared to other state-of-the-art methods.

In an era where misinformation spreads freely, factchecking (FC) plays a crucial role in verifying claims and promoting reliable information.While automated fact-checking (AFC) has advanced significantly, existing systems remain vulnerable to adversarial attacks that manipulate or generate claims, evidence, or claim-evidence pairs.These attacks can distort the truth, mislead decision-makers, and ultimately undermine the reliability of FC models.Despite growing research interest in adversarial attacks against AFC systems, a comprehensive, holistic overview of key challenges remains lacking.These challenges include understanding attack strategies, assessing the resilience of current models, and identifying ways to enhance robustness.This survey provides the first in-depth review of adversarial attacks targeting FC 1 , categorizing existing attack methodologies and evaluating their impact on AFC systems.Additionally, we examine recent advancements in adversary-aware defenses and highlight open research questions that require further exploration.Our findings underscore the urgent need for resilient FC frameworks capable of withstanding adversarial manipulations in pursuit of preserving high verification accuracy.Attack Target Edit Granularity Attack Technique Claim attack Evidence attack Claim-evidence pair attack Generate Manipulate Generate Manipulate

Fairness in Federated Learning (FL) is emerging as a critical factor driven by heterogeneous clients’ constraints and balanced model performance across various scenarios. In this survey, we delineate a comprehensive classification of the state-of-the-art fairness-aware approaches from a multifaceted perspective, i.e., model performance-oriented and capability-oriented. Moreover, we provide a framework to categorize and address various fairness concerns and associated technical aspects, examining their effectiveness in balancing equity and performance within FL frameworks. We further examine several significant evaluation metrics leveraged to measure fairness quantitatively. Finally, we explore exciting open research directions and propose prospective solutions that could drive future advancements in this important area, laying a solid foundation for researchers working toward fairness in FL.

Graph-level anomaly detection (GLAD) identifies graphs exhibiting abnormal properties within a graph dataset.Despite promising results in this task, the state-of-the-art methods cannot be fully trusted and deployed in realistic scenarios due to their black-box nature.To alleviate this, existing methods try to explain predictions by extracting important subgraphs from each graph, as instancelevel explanations.However, instance-level explanations across all samples are costly to verify and insufficient to capture the model's general behaviors.Thus, we propose a global interpretable Graph-Level Anomaly Detection model via Prototype (GLADPro), which provides global-level explanations throughout the entire dataset, that is, the significant subgraph patterns that consistently influence the model's decisions.Specifically, GLADPro incorporates prototype learning with the information bottleneck principle, enabling prototypes to capture the most significant subgraph patterns as global-level explanations through persistent interactions with key subgraphs from input graphs.In addition, a regularization term is proposed to prevent the collapse traps with theoretical proof.Finally, we filter redundant prototypes using the maximum mean discrepancy metric.Extensive experiments demonstrate the superiority of GLADPro in anomaly detection and explainability; for instance, on the mutagen dataset, it reduces the number of explanations to verify from 1403 to only 6.

Open-world object counting leverages the robust text-image alignment of pre-trained vision-language models (VLMs) to enable counting of arbitrary categories in images specified by textual queries. However, widely adopted naive fine-tuning strategies concentrate exclusively on text-image consistency for categories contained in, which leads to limited generalizability for unseen categories. In this work, we propose a plug-and-play Semantic-Driven Visual Prompt Tuning framework (SDVPT) that transfers knowledge from the training set to unseen categories with minimal overhead in parameters and inference time. First, we introduce a two-stage visual prompt learning strategy composed of Category-Specific Prompt Initialization (CSPI) and Topology-Guided Prompt Refinement (TGPR). The CSPI generates category-specific visual prompts, and then TGPR distills latent structural patterns from the VLM's text encoder to refine these prompts. During inference, we dynamically synthesize the visual prompts for unseen categories based on the semantic correlation between unseen and training categories, facilitating robust text-image alignment for unseen categories. Extensive experiments integrating SDVPT with all available open-world object counting models demonstrate its effectiveness and adaptability across three widely used datasets: FSC-147, CARPK, and PUCPR+. Code is available https://github.com/Eamon-0v0/SDVPT

Abstract Fraud detection in online networks has become increasingly challenging as fraudsters adopt sophisticated camouflage tactics to evade detection, making it imperative to combat their deceptive strategies. Graph-based fraud detection has gained significant attention in recent years, reflecting its growing potential to mitigate sophisticated fraudulent activities. The main objective of graph-based fraud detection is to distinguish between fraudsters and normal entities within graphs. While real-world networks contain complex, high-order relationships, existing graph-based fraud detection methods focus solely on pairwise interactions, overlooking non-pairwise relationships and the broader dependencies among entities within fraud graphs. Thus, we highlight the importance of exploring non-pairwise relationships to build a more effective fraud detection model. In this paper, we propose TROPICAL, a novel TRansfOrmer-based hyPergraph LearnIng framework for detecting CAmouflaged maLicious actors in online social networks. To capture comprehensive high-order relations, we construct a hypergraph from the original input graph. However, constructing the hypergraph can be computationally intensive. TROPICAL addresses this challenge by carefully selecting moderate hyperparameters, creating a balance between computational efficiency and effectively capturing high-order relationships. TROPICAL learns node representations by processing multiple hyperedge groups and incorporates positional encodings into the aggregated information to enhance their distinctiveness. The aggregated sequential information is then passed through a transformer encoder, enabling the model to generate rich, high-order representations to detect camouflaged fraudsters. Extensive experiments on two real-world datasets demonstrate TROPICAL’s superior performance compared to the state-of-the-art fraud detection models. The source codes and the datasets of our work are available at https://github.com/VenusHaghighi/TROPICAL .

Abstract In heterogeneous graph analysis, existing self-supervised learning (SSL) methods face several key challenges. Primarily, these approaches are tailored for node-level tasks and fail to effectively capture global graph-level features, a crucial aspect for comprehensive graph understanding. Furthermore, they predominantly rely on meta-path-based techniques to unravel graph structures, a process that can be computationally intensive and often intractable for complex networks. Another significant limitation is their inability to account for nonpairwise relationships, a common characteristic in real-world networks like protein-protein interaction and collaboration networks, limiting their effectiveness in graph-level learning where high-order connectivity is essential. To address these issues, we propose an innovative SSL framework for heterogeneous hypergraph embedding, expressly designed to enhance graph-level classification. Our framework introduces multi-hop attention in hypergraph convolution, a significant leap from existing attention mechanisms specifically for hypergraphs that primarily focus on immediate neighborhoods. This multi-hop approach allows for an expansive capture of relational structures, both near and far, uncovering intricate patterns integral to accurate graph-level classification. Complementing this, we implement an informative graph-level attentive pooling mechanism that surpasses traditional aggregation methods. It intelligently synthesizes features, taking into account their structural and semantic importance within the hypergraph, thereby preserving critical contextual information. Furthermore, we refine our contrastive learning approach and introduce targeted negative sampling strategies, creating a more robust learning environment that excels at discerning nuanced graph-level features. Rigorous evaluation against established graph kernels, graph neural networks, and graph pooling methods on real-world datasets demonstrates our model’s superior performance, validating its effectiveness in addressing the complexities inherent in heterogeneous graph-level classification.

Nowadays social media is the primary platform for people to obtain news and share information. Combating online fake news has become an urgent task to reduce the damage it causes to society. Existing methods typically improve their fake news detection performances by utilizing textual auxiliary information (such as relevant retweets and comments) or simple structural information ( i.e. , graph construction). However, these methods face two challenges. First, an increasing number of users tend to directly forward the source news without adding comments, resulting in a lack of textual auxiliary information. Second, simple graphs are unable to extract complex relations beyond pairwise association in a social context. Given that real-world social networks are intricate and involve high-order relations, we argue that exploring beyond pairwise relations between news and users is crucial for fake news detection. Therefore, we propose constructing an attributed hypergraph to represent non-textual and high-order relations for user participation in news spreading. We also introduce a hypergraph neural network-based method called Hy-DeFake to tackle the challenges. Our proposed method captures semantic information from news content, credibility information from involved users, and high-order correlations between news and users to learn distinctive embeddings for fake news detection. The superiority of Hy-DeFake is demonstrated through experiments conducted on four widely-used datasets, and it is compared against nine baselines using four evaluation metrics. • We introduce an approach named Hy-DeFake by constructing an attributed hypergraph to represent the process of news spreading in online social networks. By abstracting fake news detection as hyperedge classification, we capture the intricate high-order relation between news and users in social contexts, enabling us to achieve accurate results. • The proposed Hy-DeFake utilizes hypergraph neural networks for fake news detection. It effectively captures the credibility information of users and the high-order correlation between news and users. Both of these aspects provide distinctive information that contributes to fake news detection. • Extensive experiments demonstrate that Hy-DeFake generally surpasses nine baseline methods on four real-world datasets from different domains. • We verify the positive correlation between news authenticity and user credibility. Users who spread fake news exhibit more intensive interaction compared to those who spread real news, resulting in the formation of a denser community.

Session-based recommendations have become increasingly important in social media platforms due to the dynamic and temporal nature of user interactions. The utilization of Graph Neural Networks in these systems has grown due to their proficiency in incorporating node information and structural topology. However, current recommendation methods that use graphs focus on recommendations within a single session, neglecting the more complex dependencies between different sessions. This omission limits improvements in the accuracy of recommendations. In addition, existing GNN-based approaches generally focus on simple binary connections, neglecting to capture the intricate and heterogeneous interactions in real-world situations. Furthermore, a notable obstacle arises from the absence of node positional information for hyperedges in hypergraphs. Therefore, different item orders can lead to identical hyperedges, which limits the formation of precise session vector representations. The paper proposes a unified framework utilizing heterogeneous hypergraph neural networks for session-based social recommendations to address these limitations. This framework utilizes hypergraphs to depict complex multivariate connections among sessions, social networks, and items. It addresses the problem of hyperedge ambiguity while maintaining the sequential order of data. The methodology entails creating a linkage graph and a session-item graph, which aid in identifying similar user intentions across various sessions and potential behavior patterns within a single session. In addition, the framework utilizes a Graph Attention Network (GAT) to combine social information from users and their connections, thereby improving the representation of user interests. Empirical assessments on three datasets show that our proposed model outperforms popular recommendation models. This emphasizes its effectiveness in accurately capturing user preferences and behaviors in session-based social recommendations.

Social events reflect the dynamics of society and, here, natural disasters and emergencies receive significant attention. The timely detection of these events can provide organisations and individuals with valuable information to reduce or avoid losses. However, due to the complex heterogeneities of the content and structure of social media, existing models can only learn limited information; large amounts of semantic and structural information are ignored. In addition, due to high labour costs, it is rare for social media datasets to include high-quality labels, which also makes it challenging for models to learn information from social media. In this study, we propose two hyperbolic graph representation-based methods for detecting social events from heterogeneous social media environments. For cases where a dataset has labels, we design aHyperbolicSocialEventDetection (HSED) model that converts complex social information into a unified social message graph. This model addresses the heterogeneity of social media, and, with this graph, the information in social media can be used to capture structural information based on the properties of hyperbolic space. For cases where the dataset is unlabelled, we design anUnsupervisedHyperbolicSocialEventDetection (UHSED). This model is based on the HSED model but includes graph contrastive learning to make it work in unlabelled scenarios. Extensive experiments demonstrate the superiority of the proposed approaches.

In the era of data information explosion, there are different observations on an object (e.g., the height of the Himalayas) from different sources on the web, social sensing, crowd sensing, and data sensing applications. Observations from different sources on an object can conflict with each other due to errors, missing records, typos, outdated data, etc. How to discover truth facts for objects from various sources is essential and urgent. In this paper, we aim to deliver a comprehensive and exhaustive survey on truth discovery problems from the perspectives of concepts, methods, applications, and opportunities. We first systematically review and compare problems from objects, sources, and observations. Based on these problem properties, different methods are analyzed and compared in depth from observation with single or multiple values, independent or dependent sources, static or dynamic sources, and supervised or unsupervised learning, followed by the surveyed applications in various scenarios. For future studies in truth discovery fields, we summarize the code sources and datasets used in above methods. Finally, we point out the potential challenges and opportunities on truth discovery, with the goal of shedding light and promoting further investigation in this area.

The dynamic and temporal nature of user interactions on social media platforms has increased the importance of session-based recommendations. The adoption of graph neural networks in these systems has increased due to their capacity to incorporate structural topology and node information. Nevertheless, current session-based recommendation methods relying on pairwise graph structure ignore intricate and evolving interactions between users, items, and sessions across domains. However, most methodologies focus on a scenario where consumers interact with a single domain. As a result, they cannot accurately represent the intricate and evolving correlations between users and items in cross-domain scenarios. They consistently face insufficient data because they overlook the fact that users’ behaviors are distributed across domains and sessions. To address these challenges, we propose a novel dynamic hypergraph-based framework for cross-domain session-based social recommendations (DHCSRs). We construct a dynamic heterogeneous hypergraph to model the intricate higher order correlations among inter- and intradomain sessions, items, users, and social networks. In intradomain sessions, the hyperedge incorporates domain-specific features into the embedding process for user embedding by considering product categories. Interdomain sessions capture the complex interests of users who engage with diverse types of content. In addition, the framework utilizes gated recurrent units for user behavior and a domain-driven session-based RNN to capture user and session representations. The domain-driven session-based RNN exchanges information across sessions from various domains in sequential order to ensure alignment of domain interactions. The user behavior RNN learns the user’s global interests and captures behavioral differences across domains. Finally, user behavioral information and session representations are aggregated with the session and item representations from the hypergraph to predict user behavior. Comprehensive experiments demonstrate that the DHCSR model outperforms several baselines across all four datasets, with improvements ranging from 3.56% to 18.31% in HR and from 5.08% to 25.65% in NDCG, significantly enhancing the accuracy of recommendations.

The widespread diffusion of fake news has become a critical problem on dynamic social media worldwide, which requires effective strategies for fake news detection to alleviate its hazardous consequences for society. However, most recent efforts only focus on the features of news content and social context without realizing the benefits of large language models (LLMs) and multiple knowledge graphs (KGs), thus failing to improve detection capabilities further. To tackle this issue, we present a multiknowledge and LLM-inspired heterogeneous graph neural network for fake news detection (MiLk-FD), by combining KGs, LLMs, and graph neural networks (GNNs). Specifically, we first model news content as a heterogeneous graph (HG) containing news, entity, and topic nodes and then fuse the knowledge from three KGs to augment the factual basis of news articles. Meanwhile, we leverage TransE to initialize the knowledge features and employ LLaMa2-7B to obtain the initial feature vectors of news articles. After that, we utilize the devised HG transformer to learn news embeddings with specific feature distribution in high-dimensional spaces by aggregating neighborhood information according to metapaths. Finally, a classifier based on multilayer perceptron (MLP) is trained to predict each news article as fake or true. Through experiments, we demonstrate that our proposed framework surpasses ten baselines according to accuracy, precision, F1-score, recall, and ROC in four public real-world benchmarks (i.e., COVID-19, FakeNewsNet, PAN2020, Liar).

Generalized zero-shot learning (GZSL) requires that models are able to recognize classes they were trained on, and new classes they haven't seen before. Feature-generation approaches are popular due to their effectiveness in mitigating overfitting to the training classes. Existing generative approaches usually adopt simple discriminators for distribution or classification supervision, however, thus limiting their ability to generate visual features that are discriminative of and transferable to novel categories. To overcome this limitation and improve the quality of generated features, we propose a dual prototype contrastive augmented discriminator for the generative adversarial network. Specifically, we design a Dual Prototype Contrastive Network (DPCN), which leverages complementary information between visual space and semantic space through multi-task prototype contrastive learning. Contrastive learning of the visual prototypes enhances the ability of the generated features to distinguish between classes, while the contrastive learning of the semantic prototypes improves their transferability. Furthermore, we introduce margins into the contrastive learning process to ensure both intra-class compactness and inter-class separation. To demonstrate the effectiveness of the proposed approach, we conduct experiments on three widely-used zero-shot learning benchmark datasets, where DPCN achieves state-of-the-art performance for GZSL.

Recommender systems have become increasingly important in navigating the vast amount of information and options available in various domains. By tailoring and personalizing recommendations to user preferences and interests, these systems improve the user experience, efficiency, and satisfaction. With a growing demand for transparency and understanding of recommendation outputs, explainable recommender systems have gained growing attention in recent years. Additionally, as user reviews could be considered the rationales behind why the user likes (or dislikes) the products, generating informative and reliable reviews alongside recommendations has thus emerged as a research focus in explainable recommendation. However, the model-generated reviews might contain factually inconsistent contents (i.e., the hallucination issue), which would thus compromise the recommendation rationales. To address this issue, we propose a contrastive learning framework to improve the faithfulness and factuality in explainable recommendation in this article. We further develop different strategies of generating positive and negative examples for contrastive learning, such as back-translation or synonym substitution for positive examples, and editing positive examples or utilizing model-generated texts for negative examples. Our proposed method optimizes the model to distinguish faithful explanations (i.e., positive examples) and unfaithful ones with factual errors (i.e., negative examples), which thus drives the model to generate faithful reviews as explanations while avoiding inconsistent contents. Extensive experiments and analysis on three benchmark datasets show that our proposed model outperforms other review generation baselines in faithfulness and factuality. In addition, the proposed contrastive learning component could be easily incorporated into other explainable recommender systems in a plug-and-play manner.

The problem of infection source detection deals with localizing the infection source in a given network. While the problem has been extensively studied in the past, researchers have mainly focused on simulated infection networks which may not be the correct reflection of the dynamics of real-world infections. More significantly, the existing methods assume that a rumor source lies at the center of an infection network (source-centrality), which is not always true in sparse real-world rumor networks. Due to the randomness of infection flow in such networks, the source may lie away from the center (source-skewness). There is also a lack of real-world infection network datasets to provide a true real-world perspective. Therefore, we revisit the source detection problem and contemplate a shift from mainstream simulations to a real-world paradigm. To this end, we generate two novel rumor network datasets, Cov19-RN and Use20-RN, based on COVID-19 and US Elections 2020 misinformation trends on Twitter (currently$\mathbb {X}$). Besides, inspired by the technicalities inherent to real-world rumor networks, we propose a real-world oriented algorithm called Generalized Exoneration and Prominence based Age, GEPA, for rumor source detection. GEPA addresses the problem of source-skewness to detect rumor sources using the concept of generalized local prominence, which we introduce in this study. Our experiments show that GEPA significantly outperforms the state-of-the-art methods, producing detection rates of 73.6% against 61.5% of the closest competing method on Cov19-RN, and 61.5% against 52.6% of the closest competing method on Use20-RN. To the best of our knowledge, this study is the first such work to deal with source detection in real-world rumor networks and address the problem of source-skewness. Our complete source code, benchmark datasets and detailed results are available athttps://github.com/tesla121/GEPA.

Anomalies often occur in real-world information networks/graphs, such as malevolent users in online review networks and fake news in social media. When representing such structured network data as graphs, anomalies usually appear as anomalous nodes that exhibit significantly deviated structure patterns, or different attributes, or the both. To date, numerous unsupervised methods have been developed to detect anomalies based on residual analysis, which assumes that anomalies will introduce larger residual errors (i.e., graph reconstruction loss). While these existing works achieved encouraging performance, in this paper, we formally prove that their employed learning objectives, i.e., MSE and cross-entropy losses, encounter significant limitations in learning the major data distributions, particularly for anomaly detection, and through our preliminary study, we reveal that the vanilla residual analysis-based methods cannot effectively investigate the rich graph structure. Upon these discoveries, we propose a novel structure-biased graph anomaly detection framework (SALAD) to attain anomalies’ divergent patterns with the assistance of a specially designed node representation augmentation approach. We further present two effective training objectives to empower SALAD to effectively capture the major structure and attribute distributions by emphasizing less on anomalies that introduce higher reconstruction errors under the encoder-decoder framework. The detection performance on eight widely-used datasets demonstrates SALAD's superiority over twelve state-of-the-art baselines. Additional ablation and case studies validate that our data augmentation method and training objectives result in the impressive performance.

The human brain is a highly complex neurological system that has been the subject of continuous exploration by scientists. With the help of modern neuroimaging techniques, there has been significant progress made in brain disorder analysis. There is an increasing interest about utilizing artificial intelligence techniques to improve the efficiency of disorder diagnosis in recent years. However, these methods rely only on neuroimaging data for disorder diagnosis and do not explore the pathogenic mechanism behind the disorder or provide an interpretable result toward the diagnosis decision. Furthermore, the scarcity of medical data limits the performance of existing methods. As the hot application of graph neural networks (GNNs) in molecular graphs and drug discovery due to its strong graph-structured data learning ability, whether GNNs can also play a huge role in the field of brain disorder analysis. Thus, in this work, we innovatively model brain neuroimaging data into graph-structured data and propose knowledge distillation (KD) guided brain subgraph neural networks to extract discriminative subgraphs between patient and healthy brain graphs to explain which brain regions and abnormal functional connectivities cause the disorder. Specifically, we introduce the KD technique to transfer the knowledge of pretrained teacher model to guide brain subgraph neural networks training and alleviate the problem of insufficient training data. And these discriminative subgraphs are conducive to learn better brain graph-level representations for disorder prediction. We conduct abundant experiments on two functional magnetic resonance imaging datasets, i.e., Parkinson's disease (PD) and attention-deficit/hyperactivity disorder (ADHD), and experimental results well demonstrate the superiority of our method over other brain graph analysis methods for disorder prediction accuracy. The interpretable experimental results given by our method are consistent with corresponding medical research, which is encouraging to provide a potential for deeper brain disorder study.

Graph neural networks (GNNs) have shown promise in graph classification tasks, but they struggle to identify out-of-distribution (OOD) graphs often encountered in real-world scenarios, posing a significant obstacle for their open-world deployment. Due to the unpredictable nature of the various distributions to which OOD graphs adhere, the challenge of OOD graph detection lies in enabling models to capture distribution differences between in-distribution (ID) and OOD graphs. Current methods often introduce a subset of OOD patterns, such as synthetic OOD graphs, to facilitate learning the discrimination between ID and OOD graphs. However, these OOD patterns may not sufficiently encapsulate the entire range of OOD graphs, leading to inadequate learning of the distribution differences between ID and OOD graphs. In this article, we propose a novel OOD graph detection algorithm, ODGNN. The ODGNN does not expose GNNs to any OOD patterns during model training, thus reducing bias toward specific types of OOD graph samples and enhancing OOD graph detection. The algorithm differentiates graphs by evaluating whether the input graphs conform to established ID graph class-conditioned distributions. Specifically, during model training, the ODGNN integrates a Gaussian encoder into GNNs to characterize ID graph classes using distinct class-conditioned distributions. During inference, OOD graphs are mapped to a representation space distant from ID graphs due to their divergence from any known class-conditioned distribution. Extensive experiments conducted on real-world datasets validate the effectiveness of the ODGNN in enhancing OOD detection performance across various GNN-based graph classification models. The ODGNN also demonstrates superior performance compared to state-of-the-art OOD graph detection competitors.

Graph neural networks (GNNs) have been used extensively as a backbone for social recommendation. However, their application to a diverse range of situations is still rather limited. This is because graph structures only leverage pairwise user relationships. They cannot capture the higher-order relationships so common in the real world, and ignoring the interest friends and strangers might have in similar items is severely hampering the expressiveness of the current graph-based recommendation models. Hence, in this article, we outline a heterogeneous hypergraph neural network for social recommendation, called Heterogeneous Hypergraph neural network for Social Recommendation using an Attention Network (HHGSA), that incorporates an attention network to address these issues. The hypergraph is able to represent higher-order relationships through five motifs: friend and stranger item appeal, item similarity, user similarity based on interactions with items, and social relations. Two modules, the attentive vertex aggregation module and the attentive hyperedge aggregation module, capture user and item attention. In addition, it has been discovered that similar items have identical appeal when displayed to users. A GNN aggregates the user embedding data, including information about the friend and stranger and item embeddings. Finally, information about users and items is aggregated for social recommendations. Extensive experiments on four datasets demonstrate that the HHGSA model outperforms a wide range of baselines and can significantly improve the accuracy of recommendations.

Service composition dynamically integrates various services from multiple providers to meet complex user requirements. However, most existing methods assume centralized control over all services, which is often unrealistic because providers typically prefer to independently manage their own services, posing challenges to the application of traditional methods. Collaborative service composition offers a solution by enabling providers to work together to complete service composition. However, this approach also faces its own challenges. Driven by self-interest, providers may be reluctant to offer services needed by others, and due to business competition, they may wish to share as few services as possible (where sharing services means disclosing service information to other providers). To address these challenges, we propose a novel collaborative service composition approach that comprehensively considers each provider’s self-interest and achieves service composition with minimal service sharing. First, we introduce a “self-interest degree” model to capture providers’ self-interest. This behavior may lead to service refusal, so we design a service availability prediction method based on a reputation model to minimize rejections. Then, we propose a decentralized service composition method. It utilizes historical composition records to mine empirical rules between requirements and services, constructing a correlations matrix, and collaboratively trains a multi-label classification model with other providers under a distributed federated learning framework. Combining the matrix and model outputs, we design a service composition method and a node coordination protocol that completes service composition with minimal service sharing. Experimental results demonstrate the effectiveness of the proposed method in capturing providers’ self-interest and showcase its superior performance compared to existing methods.

Contrastive learning has been extensively studied in sentence representation learning as it demonstrates effectiveness in various downstream applications, where the same sentence with different dropout masks (or other augmentation methods) is considered as positive pair while taking other sentences in the same mini-batch as negative pairs. However, these methods mostly treat all negative examples equally and overlook the different similarities between the negative examples and the anchors, which thus fail to capture the fine-grained semantic information of the sentences. To address this issue, we explicitly differentiate the negative examples by their similarities with the anchor, and thus propose a simple yet effective method SoftCSE that individualizes either the weight or temperature of each negative pair in the standard InfoNCE loss according to the similarities of the negative examples and the anchors. We further provide the theoretical analysis of our methods to show why and how SoftCSE works, including the optimal solution, gradient analysis and the connection with other loss. Empirically, we conduct extensive experiments on semantic textual similarity (STS) and transfer (TR) tasks, as well as text retrieval and reranking, where we observe significant performance improvements compared to strong baseline models.

Large language models (LLMs) have emerged as valuable tools for enhancing textual features in various text-related tasks.Despite their superiority in capturing the lexical semantics between tokens for text analysis, our preliminary study on two popular LLMs, i.e., GPT-3.5 and Llama2, shows that simply applying news embeddings from LLMs is ineffective for fake news detection.Such embeddings only encapsulate the language styles between tokens.Meanwhile, the high-level semantics among named entities and topics, which reveal the deviating patterns of fake news, have been ignored.Therefore, we propose a topic model together with a set of specially designed prompts to extract topics and real entities from LLMs and model the relations among news, entities, and topics as a heterogeneous graph to facilitate investigating news semantics.We then propose a Generalized Page-Rank model and a consistent learning criterion for mining the local and global semantics centered on each news piece through the adaptive propagation of features across the graph.Our model shows superior performance on five benchmark datasets over seven baseline methods and the efficacy of the key ingredients has been thoroughly validated.1. https://api.openai.

User engagement has recently received significant attention in understanding the decay and expansion of communities in many online social networking platforms. Many user engagement studies have been conducted to find a set of critical (anchored) users in the static social network. However, social networks are highly dynamic and their structures are continuously evolving. In this paper, we target a new research problem called Anchored Vertex Tracking (AVT), aiming to track the anchored users at each timestamp of evolving networks. To address the AVT problem, we develop a greedy algorithm inspired by the previous anchored k-core study in the static networks. Furthermore, we design an incremental algorithm to efficiently solve the AVT problem by utilizing the smoothness of the network structure's evolution. The extensive experiments demonstrate the performance of our proposed algorithms.

Deep learning is increasingly crucial in scientific discovery, accelerating research in various fields. Exploring brain science using deep learning has garnered significant interest, particularly in the recognition of brain disorders. However, existing methods face limitations in the discriminability of learned brain graph representations and the identification of neurological biomarkers associated with brain disorders. Moreover, the lack of model explainability leads to suboptimal brain graph analysis. To address these challenges, we propose a bi-level brain graph contrastive learning framework with an interpretable learning kernel for brain disorder analysis. Our framework diverges from traditional graph contrastive learning methods by augmenting meaningful brain graphs using the counterfactual thinking-based mechanism, ensuring reliable graph generation that preserves discriminative information. Secondly, we introduce bi-level contrastive loss with intra-and inter-class contrasts to enhance the brain graph representation learning. Most importantly, we design an interpretable brain graph learning kernel to highlight critical regions and connections, thereby facilitating the discovery of potential neurological biomarkers associated with brain disorders in subsequent analysis. The effectiveness of our method11https://githuh.com/JustinGie/Cf-BCL in brain graph representation learning and discriminative substructure detection is demonstrated through the evaluation of disorder prediction and pathogenic analysis on three real-world brain disorder datasets. Moreover, our framework may provide novel insights into brain science based on the results of comparison with medical research.

Graph-based fraud detection has attracted increasing attention in recent years, reflecting its growing potential in mitigating sophisticated fraudulent activities. The main objective of graph-based fraud detection is to discern between fraud-sters and normal entities within graphs. As fraudsters adopt increasingly sophisticated camouflage tactics, combating them has become an urgent task. Despite the complex interactions within real-world networks involving high-order relations, ex-isting graph-based fraud detection methods often neglect non-pairwise relationships among entities in graphs. Thus, we empha-size the significance of investigating beyond pairwise relationships for building an effective fraud detection model. In this paper, we propose constructing a hypergraph from the original input graph to encapsulate comprehensive high-order relations and present TROPICAL, a novel TRansfOrmer-based hyPergraph LearnIng for detecting CAmouflaged maLicious actors in online social networks. TROPICAL learns representations by processing different hyperedge groups and incorporates positional encodings into the aggregated information to enhance their distinctiveness. Subsequently, the model feeds the learned aggregated sequential information into the transformer encoder, achieving rich rep-resentations for effective camouflaged fraudster detection. The superiority of TROPICAL is demonstrated through experiments conducted on two real-world datasets, compared against the state-of-the-art fraud detection models. The source codes and datasets of our work are available at https://github.comNenusHaghighi/TROPICAL.

Since the inception of online fake news detection, the technique of natural language processing has predominantly been leading the field by utilizing text classification to discern veracity. From the network perspective, news traveling within a social network typically exhibits non-textual correlations aligned with the network of news propagation or news-user interaction. Therefore, with the advancement of graph learning, there have been emerging approaches incorporating graphs of social contexts as auxiliary information, of which the performance still relies on learning semantics from news text. As fake news becomes more adept at employing the writing pattern of real news and the assessment of certain news contents requires domain-specific knowledge, distinguishing real news from fake ones based on the text has become increasingly challenging. This raises a question: Can we debunk fake news without going through the text? Thus, this work aims to explore the feasibility of differentiating between real and fake news by capturing its relationships with other news and people in the network. We propose a method named ComE-DeFake which extracts intricate relations beyond pairwise of news and users in social contexts to detect fake news. Experimental results reveal that our method without using news text outperforms all baseline methods. This suggests that, if high-order complicated relations are fully captured, it is achievable to debunk fake news without analyzing its text.

With the prompt growth of digital data, a newly evolving economy has shown its development trend in today’s data-driven world, which is known as the data economy. Stimulating this data economy and seizing benefits from it remains an open question. This paper delves into feasible breakthroughs prompting this emerging data economy in supplying data catalogue services for data products. Accordingly, the opportunities and potential for the application of natural language processing technology in data catalogues are envisaged, corresponding to challenges posed by various types of data assets. By exploring the state-of-the-art natural language processing fields, this paper introduces the latest advancements in knowledge-aware natural language processing for contributing to data catalogues.

Anomalous node detection in a static graph faces significant challenges due to the rarity of anomalies and the substantial cost of labeling their deviant structure and attribute patterns. These challenges give rise to data-centric problems, including extremely imbalanced data distributions and intricate graph learning, which significantly impede machine learning and deep learning methods from discerning the patterns of graph anomalies with few labels. While these issues remain crucial, much of the current research focuses on addressing the induced technical challenges, treating the shortage of labeled data as a given. Distinct from previous efforts, this work focuses on tackling the data-centric problems by generating auxiliary training nodes that conform to the original graph topology and attribute distribution. We categorize this approach as data-centric, aiming to enhance existing anomaly detectors by training them on our synthetic data. However, the methods for generating nodes and the effectiveness of utilizing synthetic data for graph anomaly detection remain unexplored in the realm. To answer these questions, we thoroughly investigate the denoising diffusion model. Drawing from our observations on the diffusion process, we illuminate the shifts in graph energy distribution and establish two principles for designing denoising neural networks tailored to graph anomaly generation. From the insights, we propose a diffusion-based graph generation method to synthesize training nodes, which can be promptly integrated to work with existing anomaly detectors. The empirical results on eight widely-used datasets demonstrate our generated data can effectively enhance the nine state-of-the-art graph detectors' performance.

Haojie Zhuang, Wei Emma Zhang, Leon Xie, Weitong Chen, Jian Yang, Quan Sheng. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

In this paper, we propose an interpretable brain graph contrastive learning framework, which aims to learn brain graph representations by a unsupervised way for disorder prediction and pathogenic analysis. Our framework consists of two key designs: We first utilize the controllable data augmentation strategy to perturb unimportant structures and attribute features for the generation of brain graphs. Then, considering that the difference of healthy and patient brain graphs is small, we introduce hard negative sample evaluation to weight negative samples of the contrastive loss, which can learn more discriminative brain graph representations. More importantly, our method can observe salient brain regions and connections for pathogenic analysis. We conduct disorder prediction and interpretable analysis experiments on three real-world neuroimaging datasets to demonstrate the effectiveness of our framework.

Social events reflect changes in communities, such as natural disasters and emergencies. Detection of these situations can help residents and organizations in the community avoid danger and reduce losses. The complex nature of social messages makes social event detection on social media challenging. The challenges that have a greater impact on social media detection models are as follows: (1) the amount of social media data is huge but its availability is small; (2) social media data is a tree structure and traditional Euclidean space embedding will distort embedded features; and (3) the heterogeneity of social media networks makes existing models unable to capture rich information well. To solve the above challenges, we propose a Heterogeneous Information Graph representation via Hyperbolic space combined with an Automatic Meta-path selection (GraphHAM) model, an efficient framework that automatically selects the meta-path's weight and combines hyperbolic space to learn information on social media. In particular, we apply an efficient automatic meta-path selection technique and convert the selected meta-path into a vector, thereby reducing the requisite amount of labeled data for the model. We also design a novel Hyperbolic Multi-Layer Perceptron (HMLP) to further learn the semantic and structural information of social information. Extensive experiments show that GraphHAM can achieve outstanding performance on real-world data using only 20% of the whole dataset as the training set. Our code can be found on GitHub https://github.com/ZITAIQIU/GraphHAM.

Fake news is pervasive on social media, inflicting substantial harm on public discourse and societal well-being. We investigate the explicit structural information and textual features of news pieces by constructing a heterogeneous graph concerning the relations among news topics, entities, and content. Through our study, we reveal that fake news can be effectively detected in terms of the atypical heterogeneous subgraphs centered on them, which encapsulate the essential semantics and intricate relations between news elements. However, suffering from the heterogeneity, exploring such heterogeneous subgraphs remains an open problem. To bridge the gap, this work proposes a heterogeneous subgraph transformer HeteroSGT to exploit subgraphs in our constructed heterogeneous graph. In HeteroSGT, we first employ a pre-trained language model to derive both word-level and sentence-level semantics. Then the random walk with restart (RWR) is applied to extract subgraphs centered on each news, which are further fed to our proposed subgraph Transformer to quantify the authenticity. Extensive experiments on five real-world datasets demonstrate the superior performance of HeteroSGT over five baselines. Further case and ablation studies validate our motivation and demonstrate that performance improvement stems from our specially designed components.

Influence Maximization(IM), which aims to select a set of users from a social network to maximize the expected number of influenced users, has recently received significant attention for mass communication and commercial marketing. Existing research efforts dedicated to the IM problem depend on a strong assumption: the selected seed users are willing to spread the information after receiving benefits from a company or organization. In reality, however, some seed users may be reluctant to spread the information or need to be paid higher to be motivated. Furthermore, the existing IM works pay little attention to capture users’ influence propagation in the future period. In this paper, we target a new research problem named,ReconnectingTop-$l$lRelationships(RT$l$R) query, which aims to find$l$number of previous existing relationships but being estranged later such that reconnecting these relationships will maximize the expected number of influenced users by the given group in a future period. We prove that the RT$l$R problem is NP-hard. An efficient greedy algorithm is proposed to answer the RT$l$R queries with the influence estimation technique and the well-chosen link prediction method to predict the near future network structure. We also design a pruning method to reduce unnecessary probing from candidate edges. Further, a carefully designed order-based algorithm is proposed to accelerate the RT$l$R queries. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness and efficiency of our proposed methods.

Graph pooling technique as the essential component of graph neural networks has gotten increasing attention recently and it aims to learn graph-level representations for the whole graph. Besides, graph pooling is important in graph classification and graph generation tasks. However, current graph pooling methods mainly coarsen a sequence of small-sized graphs to capture hierarchical structures, potentially resulting in the deterioration of the global structure of the original graph and influencing the quality of graph representations. Furthermore, these methods artificially select the number of graph pooling layers for different graph datasets rather than considering each graph individually. In reality, the structure and size differences among graphs necessitate a specific number of graph pooling layers for each graph. In this work, we propose reinforced pooling graph neural networks via adaptive hybrid graph coarsening networks. Specifically, we design a hybrid graph coarsening strategy to coarsen redundant structures of the original graph while retaining the global structure. In addition, we introduce multi-agent reinforcement learning to adaptively perform the graph coarsening process to extract the most representative coarsened graph for each graph, enhancing the quality of graph-level representations. Finally, we design graph-level contrast to improve the preservation of global information in graph-level representations. Extensive experiments with rich baselines on six benchmark datasets show the effectiveness of ReiPool1.

Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis. However, beyond the classic spectral clustering and statistical inference methods, there have been significant developments with deep learning techniques for community detection in recent years-particularly when it comes to handling high-dimensional network data. Hence, a comprehensive review of the latest progress in community detection through deep learning is timely. To frame the survey, we have devised a new taxonomy covering different state-of-the-art methods, including deep learning models based on deep neural networks (DNNs), deep nonnegative matrix factorization, and deep sparse filtering. The main category, i.e., DNNs, is further divided into convolutional networks, graph attention networks, generative adversarial networks, and autoencoders. The popular benchmark datasets, evaluation metrics, and open-source implementations to address experimentation settings are also summarized. This is followed by a discussion on the practical applications of community detection in various domains. The survey concludes with suggestions of challenging topics that would make for fruitful future research directions in this fast-growing deep learning field.

Special Issue Part 1 (Issue 3) and Part 2 (Issue 4) of AIEDAM are based on a workshop on Learning and Creativity held at the 2002 conference on Artificial Intelligence in Design, AID '02 (www.cad.strath.ac.uk/AID02_workshop/Workshop_webpage.html; Gero, ...

Social Event Detection has become increasingly important as a way of letting the public know about significant global events in a clear and timely fashion. However, current methods are still unsatisfactory for detecting events due to the complexity of reality social networks. Specifically, there exist overlinks for partial nodes (i.e., messages), and conversely, some nodes lack sufficient neighborhood information. In addition, the time interval between different messages in social networks should also be fully considered by existing shallow GNN models. What is needed is an approach that can improve the embedding ability of GNN models by extracting and aggregating more se-mantic and structural information from the same graph for more representative and robust message embeddings, rather than constructing a richer message graph by adding additional attributed data. To this end, we design an innovative Reinforced Deep Heterogeneous Graph Attention Network (Re-DHAN) method for offline and incremental social event detection tasks. The framework initially leverages multi-agent reinforcement learning to select the most meaningful neighbors from various meta-path graphs to avoid redundant links. Then, our approach adds temporal convolutional attention across the semantics and timestamps for all nodes, ensuring a comprehensive capture of both semantic and time elements within social networks. Finally, to ensure that the embeddings produced by DHAN are both robust to too few neighbors and highly representative, we constructed a new Dual Graph Contrastive Learning model, called DGCL, to simulate the missing structural information. DGCL includes a triplet loss function and an unsupervised GraphCL model with multi-scaled subgraph augmentation. A series of experiments in offline and incremental social event detection demonstrates Re-DHAN as superior to the current state-of-the-art baselines. Furthermore, the results show that our ReDHAN has powerful and robust abilities to capture, extract, and aggregate the semantic and structural information within graphs.

Piecewise Linear Approximation (PLA) is one of the most widely used approaches for representing a time series with a set of approximated line segments. With this compressed form of representation, many large complicated time series can be efficiently stored, transmitted and analyzed. In this article, with the introduced concept of "semi-connection" that allowing two representation lines to be connected at a point between two consecutive time stamps, we propose a new optimal linear-time PLA algorithm SemiOptConnAlg for generating the least number of semi-connected line segments with guaranteed maximum error bound. With extended experimental tests, we demonstrate that the proposed algorithm is very efficient in execution and achieves better performances than the state-of-art solutions.

Representation learning in unlabeled heterogeneous graphs has gained significant interest. The heterogeneity in graphs not only provides rich information but also poses challenges to model complex relations in self-supervised learning (SSL) manner. Existing SSL-based approaches are usually designed for node-level tasks and are unable to capture global graph-level features. Also, they often employ computationally expensive meta-path-based techniques, to learn the intrinsic graph structure, that are intractable. Importantly, they overlook non-pairwise relationships among nodes in heterogeneous graphs, for instance in protein-protein interaction networks or collaboration networks, limiting the effectiveness of graph-level learning. To address these issues, we propose a novel self-supervised heterogeneous hypergraph learning framework that captures the richness of heterogeneity, and high-order connectivity in graph-level classification. Unlike traditional methods that rely on meta-path-based approaches to incorporate high-order information, we introduce a k-hop neighborhood strategy to construct intra-graph hyperedges, and a shared attribute-based approach for inter-graph hyperedges to construct the heterogeneous hypergraph. Furthermore, we introduce a context-aware graph-level pooling mechanism that facilitates adaptive aggregation of relevant information across the hypergraph, considering both local and global contexts. Lastly, we design a self-supervised contrastive learning framework by introducing a high-order-aware adaptive augmentation mechanism. This enables the model to learn meaningful graph-level representations from less-labeled data. We evaluate our proposed model against graph kernels, graph neural networks, and graph pooling-based baselines on real-world datasets, demonstrating an overall performance improvement of 5.81% that validates the effectiveness and superiority of the proposed method.

Generative Pre-trained Transformer (GPT) is a state-of-the-art machine learning model capable of generating human-like text through natural language processing (NLP). GPT is trained on massive amounts of text data and uses deep learning techniques to learn patterns and relationships within the data, enabling it to generate coherent and contextually appropriate text. This position paper proposes using GPT technology to generate new process models when/if needed. We introduce ProcessGPT as a new technology that has the potential to enhance decision-making in data-centric and knowledge-intensive processes. ProcessGPT can be designed by training a generative pre-trained transformer model on a large dataset of business process data. This model can then be fine-tuned on specific process domains and trained to generate process flows and make decisions based on context and user input. The model can be integrated with NLP and machine learning techniques to provide insights and recommendations for process improvement. Furthermore, the model can automate repetitive tasks and improve process efficiency while enabling knowledge workers to communicate analysis findings, support evidence, and make decisions. ProcessGPT can revolutionize business process management (BPM) by offering a powerful tool for process automation and improvement. Finally, we demonstrate how ProcessGPT can be a powerful tool for augmenting data engineers in maintaining data ecosystem processes within large bank organizations. Our scenario highlights the potential of this approach to improve efficiency, reduce costs, and enhance the quality of business operations through the automation of data-centric and knowledge-intensive processes. These results underscore the promise of ProcessGPT as a transformative technology for organizations looking to improve their process workflows.

Smooth Inter-organization or cross-domain data flows are increasingly demanded when more organizations are becoming data-driven, which will eventually lead to the rise of a new form of the world economy, that is, data economy (DE). Through exploring relevant concepts, technologies, issues and challenges facing DE, this paper discusses the operational concepts of DE ecosystem and its key elements, particularly data products and data marketplaces, and the trends of their developments. It then presents directions for research and development of DE ecosystem and its key elements, in order to help organizations and industry to prepare for the arrival of DE and identify areas for innovations and disruptive technologies. The paper also discusses the requirements of the data product design in the enterprise data ecosystem (EDE) for the rising data economy and the gaps that exist in the current data engineering and technologies, particularly examining the role of service technology in the development and practice of data economy ecosystems.

Due to information explosion, conflicting data on the same object among multiple sources is ubiquitous on the Web. To solve those conflicts while estimating source reliability, truth discovery has become a hot topic. However, when considering multi-value objects, the inevitable unbalanced data distribution is overlooked by the existing approaches. In particular, only a few sources make lots of claims while most sources only provide a few claims, which renders the source reliability estimated for “small” sources totally random; Some objects are covered by plenty of sources while some objects are claimed by only a few sources, which causes the value correctness calculated for “cold” objects unreasonable. To tackle the unbalanced data where multi-value objects exist, we propose a confidence interval based approach (CIMTD). We estimate source reliability from two aspects, i.e., the ability to claim the correct number of value(s) and specific value(s) on an object. To reflect the real reliability for both “big” and “small” sources, confidence intervals of enriched estimation are considered. While estimating source reliability, uncertainty degrees are introduced to model object differences. Confidence intervals are also considered to reflect the real uncertainty for both “hot” and “cold” objects. Experimental results on two real-world datasets demonstrate the effectiveness of our approach.

Graph-level anomaly detection aims at capturing anomalous individual graphs in a graph set. Due to its significance in various real-world application fields, e.g., identifying rare molecules in chemistry and detecting potential frauds in online social networks, graph-level anomaly detection has received great attention recently. In distinction from node- and edge-level anomaly detection that is devoted to identifying anomalies on a single graph, graph-level anomaly detection faces more significant challenges because both the intra- and inter- graph structural and attribute patterns need to be taken into account to distinguish anomalies that exhibit deviating structures, rare attributes or the both. Although deep graph representation learning shows effectiveness in fusing high-level representations and capturing characters of individual graphs, most of the existing works are defective in graph-level anomaly detection because of their limited capability in exploring information across graphs, the imbalanced data distribution of anomalies, and low interpretability of the black-box graph neural networks (GNNs). To overcome these limitations, we propose a novel deep evolutionary graph mapping framework named GmapAD1, which can adaptively map each graph into a new feature space based on its similarity to a set of representative nodes chosen from the graph set. By automatically adjusting the candidate nodes using a specially designed evolutionary algorithm, anomalies and normal graphs are mapped to separate areas in the new feature space where a clear boundary between them can be learned. The selected candidate nodes can therefore be regarded as a benchmark for explaining anomalies because anomalies are more dissimilar/similar to the benchmark than normal graphs. Through our extensive experiments on nine real-world datasets, we demonstrate that exploring both intra- and inter- graph structural and attribute information is critical to spot anomalous graphs, and our method has achieved statistically significant improvements compared to the state of the art in terms of precision, recall, F1 score, and AUC.

Cross-Domain Recommendation (CDR) is proposed to address the long-standing data sparsity problem in recommender systems (RSs). Traditional CDR only leverages relatively richer information from an auxiliary domain to improve the performance in a sparser domain, which is also called single-target CDR. In recent years, dual-target CDR has been proposed to improve recommendation performance in both domains simultaneously. The existing dual-target CDR methods are based on common users to achieve knowledge transfer between domains. We argue that the existing methods face two challenges: (1) how to learn more representative user and item embeddings in each domain, and (2) in the case of a small number of common users in real-world datasets, how to achieve better knowledge transfer. To address these challenges, in this paper, we propose a contrastive learning (CL) framework, called CL-DTCDR. In CL-DTCDR, we first design a CL task in each domain to learn more representative user and item embeddings. Then, we further construct positive pairs of the user and her/his most similar user between domains to optimize user embeddings. By two CL tasks, CL-DTCDR effectively improves performance in both domains. Extensive experiments conducted on three real-world datasets demonstrate that CL-DTCDR significantly outperforms the state-of-the-art approaches.

The proliferation of fake news in social media has been recognized as a severe problem for society, and substantial attempts have been devoted to fake news detection to alleviate the detrimental impacts. Knowledge graphs (KGs) comprise rich factual relations among real entities, which could be utilized as ground-truth databases and enhance fake news detection. However, most of the existing methods only leveraged natural language processing and graph mining techniques to extract features of fake news for detection and rarely explored the ground knowledge in knowledge graphs. In this work, we propose a novel Heterogeneous Graph Neural Network via Knowledge Relations for Fake News Detection (HGNNR4FD). The devised framework has four major components: 1) A heterogeneous graph (HG) built upon news content, including three types of nodes, i.e., news, entities, and topics, and their relations. 2) A KG that provides the factual basis for detecting fake news by generating embeddings via relations in the KG. 3) A novel attention-based heterogeneous graph neural network that can aggregate information from HG and KG, and 4) a fake news detector, which is capable of identifying fake news based on the news embeddings generated by HGNNR4FD. We further validate the performance of our method by comparison with seven state-of-art baselines and verify the effectiveness of the components through a thorough ablation analysis. From the results, we empirically demonstrate that our framework achieves superior results and yields improvement over the baselines regarding evaluation metrics of accuracy, precision, recall, and F1-score on four real-world datasets.

Users' involvement in creating and propagating news is a vital aspect of fake news detection in online social networks. Intuitively, credible users are more likely to share trustworthy news, while untrusted users have a higher probability of spreading untrustworthy news. In this paper, we construct a dual-layer graph (i.e., news layer and user layer) to extract multi-relations of news and users in social networks to derive rich information for detecting fake news. Based on the dual-layer graph, we propose a fake news detection model Us-DeFake. It learns the propagation features of news in the news layer and the interaction features of users in the user layer. Through the inter-layer in the graph, Us-DeFake fuses the user signals that contain credibility information into the news features, to provide distinctive user-aware embeddings of news for fake news detection. The training process conducts on multiple dual-layer subgraphs obtained by a graph sampler to scale Us-DeFake in large scale social networks. Extensive experiments on real-world datasets illustrate the superiority of Us-DeFake which outperforms all baselines, and the users' credibility signals learned by interaction relation can notably improve the performance of our model.

Graph neural networks (GNNs) are now the mainstream method for mining graph-structured data and learning low-dimensional node- and graph-level embeddings to serve downstream tasks. However, limited by the bottleneck of interpretability that deep neural networks present, existing GNNs have ignored the issue of estimating the appropriate number of dimensions for the embeddings. Hence, we propose a novel framework called Minimum Graph Entropy principle-guided Dimension Estimation, i.e. MGEDE, that learns the appropriate embedding dimensions for both node and graph representations. In terms of node-level estimation, a minimum entropy function that counts both structure and attribute entropy, appraises the appropriate number of dimensions. In terms of graph-level estimation, each graph is assigned a customized embedding dimension from a candidate set based on the number of dimensions estimated for the node-level embeddings. Comprehensive experiments with node and graph classification tasks and nine benchmark datasets verify the effectiveness and generalizability of MGEDE.

Tracking the evolution of clusters in social media streams is becoming increasingly important for many applications, such as early detection and monitoring of natural disasters or pandemics. In contrast to clustering on a static set of data, streaming data clustering does not have a global view of the complete data. The local (or partial) view in a high-speed stream makes clustering a challenging task. In this paper, we propose a novel density peak based algorithm,TStream, for tracking the evolution of clusters and outliers in social media streams, via the evolutionary actions of cluster adjustment, emergence, disappearance, split, and merge.TStreamis based on a temporal decay model and text stream summarisation. The decay model captures the decreasing importance of textual documents over time. The stream summarisation compactly represents them with the help of cells (akamicro-clusters) in the memory. We also propose a novel efficient index calledshared dependency tree(akaSD-Tree) based on the ideas of density peak and shared dependency. It maintains the dynamic dependency relationships inTStreamand thereby improves the overall efficiency. We conduct extensive experiments on five real datasets.TStreamoutperforms the existing state-of-the-art solutions based onMStream,MStreamF,EDMStream,OSGM, andEStream, in terms of cluster mapping measure (CMM) by up to 17.8%, 18.6%, 6.9%, 16.4%, and 20.1%, respectively. It is also significantly more efficient thanMStream,MStreamF,OSGM, andEStream, in terms of response time and throughput.

User engagement has recently received significant attention in understanding the decay and expansion of communities in many online social networking platforms. When a user chooses to leave a social networking platform, it may cause a cascading dropping out among her friends. In many scenarios, it would be a good idea to persuade critical users to stay active in the network and prevent such a cascade because critical users can have significant influence on user engagement of the whole network. Many user engagement studies have been conducted to find a set of critical(anchored)users in the static social network. However, social networks are highly dynamic and their structures are continuously evolving. In order to fully utilize the power of anchored users in evolving networks, existing studies have to mine multiple sets of anchored users at different times, which incurs an expensive computational cost. To better understand user engagement in evolving network, we target a new research problem calledAnchored Vertex Tracking(AVT) in this paper, aiming to track the anchored users at each timestamp of evolving networks. Nonetheless, it is nontrivial to handle the AVT problem which we have proved to be NP-hard. To address the challenge, we develop a greedy algorithm inspired by the previous anchored$k$-core study in the static networks. Furthermore, we design an incremental algorithm to efficiently solve the AVT problem by utilizing the smoothness of the network structure's evolution. The extensive experiments conducted on real and synthetic datasets demonstrate the performance of our proposed algorithms and the effectiveness in solving the AVT problem.

Anomalies are rare observations (e.g., data records or events) that deviate significantly from the others in the sample. Over the past few decades, research on anomaly mining has received increasing interests due to the implications of these occurrences in a wide range of disciplines - for instance, security, finance, and medicine. For this reason, anomaly detection, which aims to identify these rare observations, has become one of the most vital tasks in the world and has shown its power in preventing detrimental events, such as financial fraud, network intrusions, and social spam. The detection task is typically solved by identifying outlying data points in the feature space, which, inherently, overlooks the relational information in real-world data. At the same time, graphs have been prevalently used to represent the structural/relational information, which raises thegraph anomaly detection problem- identifying anomalous graph objects (i.e., nodes, edges and sub-graphs) in a single graph, or anomalous graphs in a set/database of graphs. Conventional anomaly detection techniques cannot tackle this problem well because of the complexity of graph data (e.g., irregular structures, relational dependencies, node/edge types/attributes/directions/multiplicities/weights, large scale, etc.). However, thanks to the advent of deep learning in breaking these limitations, graph anomaly detection with deep learning has received a growing attention recently. In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection. Specifically, we provide a taxonomy that follows a task-driven strategy and categorizes existing work according to the anomalous graph objects that they can detect. We especially focus on the challenges in this research area and discuss the key intuitions, technical details as well as relative strengths and weaknesses of various techniques in each category. From the survey results, we highlight 12 future research directions spanning unsolved and emerging problems introduced by graph data, anomaly detection, deep learning and real-world applications. Additionally, to provide a wealth of useful resources for future studies, we have compiled a set of open-source implementations, public datasets, and commonly-used evaluation metrics. With this survey, our goal is to create a “one-stop-shop” that provides a unified understanding of the problem categories and existing approaches, publicly available hands-on resources, and high-impact open challenges for graph anomaly detection using deep learning.

Classification methods for streaming data are not new, but very few current frameworks address all three of the most common problems with these tasks: concept drift, noise, and the exorbitant costs associated with labeling the unlabeled instances in data streams. Motivated by this gap in the field, we developed an active learning framework based on a dual-query strategy and Ebbinghaus's law of human memory cognition. Called CogDQS, the query strategy samples only the most representative instances for manual annotation based on local density and uncertainty, thus significantly reducing the cost of labeling. The policy for discerning drift from noise and replacing outdated instances with new concepts is based on the three criteria of the Ebbinghaus forgetting curve: recall, the fading period, and the memory strength. Simulations comparing CogDQS with baselines on six different data streams containing gradual drift or abrupt drift with and without noise show that our approach produces accurate, stable models with good generalization ability at minimal labeling, storage, and computation costs.

Trust prediction provides valuable support for decision making, information dissemination, and product promotion in online social networks. As a complex concept in the social network community, trust relationships among people can be established virtually based on: 1) their interaction behaviors, e.g., the ratings and comments that they provided; 2) the contextual information associated with their interactions, e.g., location and culture; and 3) the relative temporal features of interactions and the time periods when the trust relationships hold. Most of the existing works only focus on some aspects of trust, and there is not a comprehensive study of user trust development that considers and incorporates 1)-3) in trust prediction. In this article, we propose a context-aware deep trust prediction model C-DeepTrust to fill this gap. First, we conduct user feature modeling to obtain the user's static and dynamic preference features in each context. Static user preference features are obtained from all the ratings and reviews that a user provided, while dynamic user preference features are obtained from the items rated/reviewed by the user in time series. The obtained context-aware user features are then combined and fed into the multilayer projection structure to further mine the context-aware latent features. Finally, the context-aware trust relationships between users are calculated by their context-aware feature vector cosine similarities according to the social homophily theory, which shows a pervasive property of social networks that trust relationships are more likely to be developed among similar people. Extensive experiments conducted on two real-world datasets show the superior performance of our approach compared with the representative baseline methods.

No abstract available.

Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Receiving increasing attention from both academia and industry, yet existing research on this task still suffers from two critical issues when learning informative anomalous behavior from graph data. For one thing, anomalies are usually hard to capture because of their subtle abnormal behavior and the shortage of background knowledge about them, which causes severe anomalous sample scarcity. Meanwhile, the overwhelming majority of objects in real-world graphs are normal, bringing the class imbalance problem as well. To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes. A series of experiments on three datasets prove that DAGAD outperforms ten state-of-the-art baseline detectors concerning various mostly-used metrics, together with an extensive ablation study validating the strength of our proposed modules.

Point-of-interest (POI) recommendations can help users explore attractive locations, which is playing an important role in location-based social networks (LBSNs). In POI recommendations, the results are largely impacted by users' preferences. However, the existing POI methods model user and location almost separately, which cannot capture users' personal and dynamic preferences to location. In addition, they also ignore users' acceptance to distance/time of location. To overcome the limitations of the existing methods, we first introduce Knowledge Graph with temporal information (known as TKG) into POI recommendation, including both user and location with timestamps. Then, based on TKG, we propose a Spatial-Temporal Graph Convolutional Attention Network (STGCAN), a novel network that learns users' preferences on TKG by dynamically capturing the spatial-temporal neighbourhoods. Specifically, in STGCAN, we construct receptive fields on TKG to aggregate neighbourhoods of user and location respectively at each timestamp. And we measure the spatial-temporal interval as users' acceptance to distance/time with self-attention. Experiments on three real-world datasets demonstrate that the proposed model outperforms the state-of-the-art POI recommendation approaches.

Graph-level anomaly detection aims to distinguish anomalous graphs in a graph dataset from normal graphs. Anomalous graphs represent a very few but essential patterns in the real world. The anomalous property of a graph may be referable to its anomalous attributes of particular nodes and anomalous substructures that refer to a subset of nodes and edges in the graph. In addition, due to the imbalance nature of anomaly problem, anomalous information will be diluted by normal graphs with overwhelming quantities. Various anomaly notions in the attributes and/or substructures and the imbalance nature together make detecting anomalous graphs a non-trivial task. In this paper, we propose a graph neural network for graph-level anomaly detection, namely iGAD. Specifically, an anomalous graph attribute-aware graph convolution and an anomalous graph substructure-aware deep Random Walk Kernel (deep RWK) are welded into a graph neural network to achieve the dual-discriminative ability on anomalous attributes and substructures. Deep RWK in iGAD makes up for the deficiency of graph convolution in distinguishing structural information caused by the simple neighborhood aggregation mechanism. Further, we propose a Point Mutual Information (PMI)-based loss function to target the problems caused by imbalance distributions. PMI-based loss function enables iGAD to capture essential correlation between input graphs and their anomalous/normal properties. We evaluate iGAD on four real-world graph datasets. Extensive experiments demonstrate the superiority of iGAD on the graph-level anomaly detection task.

This paper demonstrates Fair-SRS, a Fair Session-based Recommendation System that predicts user's next click based on their historical and current sessions. Fair-SRS provides personalized and diversified recommendations in two main steps: (1) forming user's session graph embeddings based on their long- and short-term interests, and (2) computing user's level of interest in diversity based on their recently-clicked items' similarity. In real-world scenarios, users tend to interact with more or fewer contents at different times, and providers expect to receive more exposure for their items. To achieve the objectives of both sides, the proposed Fair-SRS optimizes recommendations by making a trade-off between accuracy and personalized diversity.

Graph anomaly detection, here, aims to find rare patterns that are significantly different from other nodes. Attributed graphs containing complex structure and attribute information are ubiquitous in our life scenarios such as bank account transaction graph and paper citation graph. Anomalous nodes on attributed graphs show great difference from others in the perspectives of structure and attributes, and give rise to various types of graph anomalies. In this paper, we investigate three types of graph anomalies: local, global, and structure anomalies. And, graph neural networks (GNNs) based anomaly detection methods attract considerable research interests due to the power of modeling attributed graphs. However, the convolution operation of GNNs aggregates neighbors information to represent nodes, which makes node representations more similar and cannot effectively distinguish between normal and anomalous nodes, thus result in sub-optimal results. To improve the performance of anomaly detection, we propose a novel community-aware attributed graph anomaly detection framework (ComGA). We design a tailored deep graph convolutional network (tGCN) to anomaly detection on attributed graphs. Extensive experiments on eight real-life graph datasets demonstrate the effectiveness of ComGA.

Piecewise Linear Approximation (PLA) is one of the most widely used approaches for representing a time series with a set of approximated line segments. With this compressed form of representation, many large complicated time series can be efficiently stored, transmitted and analyzed. In this article, with the introduced concept of “semi-connection” that allowing two representation lines to be connected at a point between two consecutive time stamps, we propose a new optimal linear-time PLA algorithm SemiOptConnAlg for generating the least number of semi-connected line segments with guaranteed maximum error bound. With extended experimental tests, we demonstrate that the proposed algorithm is very efficient in execution time and achieves better performances than the state-of-art solutions.

With the development of e-commerce, fraud behaviors have been becoming one of the biggest threats to the e-commerce business. Fraud behaviors seriously damage the ranking system of e-commerce platforms and adversely influence the shopping experience of users. It is of great practical value to detect fraud behaviors on e-commerce platforms. However, the task is non-trivial, since the adversarial action taken by fraudsters. Existing fraud detection systems used in the e-commerce industry easily suffer from performance decay and can not adapt to the upgrade of fraud patterns, as they take already known fraud behaviors as supervision information to detect other suspicious behaviors. In this article, we propose a competitive graph neural networks (CGNN)-based fraud detection system (eFraudCom) to detect fraud behaviors at one of the largest e-commerce platforms, “Taobao” 1 . In the eFraudCom system, (1) the competitive graph neural networks (CGNN) as the core part of eFraudCom can classify behaviors of users directly by modeling the distributions of normal and fraud behaviors separately; (2) some normal behaviors will be utilized as weak supervision information to guide the CGNN to build the profile for normal behaviors that are more stable than fraud behaviors. The algorithm dependency on fraud behaviors will be eliminated, which enables eFraudCom to detect fraud behaviors in presence of the new fraud patterns; (3) the mutual information regularization term can maximize the separability between normal and fraud behaviors to further improve CGNN. eFraudCom is implemented into a prototype system and the performance of the system is evaluated by extensive experiments. The experiments on two Taobao and two public datasets demonstrate that the proposed deep framework CGNN is superior to other baselines in detecting fraud behaviors. A case study on Taobao datasets verifies that CGNN is still robust when the fraud patterns have been upgraded.

Detecting and understanding the differences among process models is important for business improvement. Most of the existing work in analysing the differences between two process models employs an edit script approach, i.e., using a sequence of edit operations that transform one to another by applyingdeleteorinsertoperations. However, describing process differences this way is hard for users to understand and interpret. To overcome the problem, we propose a pattern-based method for process difference detection namedProDiff. We specify a set of process difference patterns as Single-Entry-Single-Exit (SESE) fragments of a process model. Process differences are detected by decomposing process models into different levels of SESE fragments, based on whichProDifflocates the positions of differences and provides assistance for users to carry out further analysis. A case study is provided to show the effectiveness and extensibility of the proposed method.

The objective of fraud detection is to distinguish fraudsters from normal users. In graph/network environments, both fraudsters and normal users are modeled as nodes, and the connections between those nodes are represented as edges. Fraudsters typically try to camouflage themselves with “normal” behaviors, say, by deliberately establishing many connections to normal users. Such camouflage inherently makes their appearance inconsistent with the essence of what it is to be normal, and gives rise to inconsistencies in the graph. In this paper, we investigate three aspects of these graph inconsistencies: features, topologies, and relations. To date, graph-based fraud detectors have shown a rather limited capability to fuse information about different types of inconsistencies. Apart from that, there is another problem of imbalance to overcome. This is because fraudsters usually only account for a very small percentage of all users. To achieve a promising capability, i.e., dual-resistant to graph inconsistency and imbalance, we present a new fraud detection model FRAUDRE based on Graph Neural Networks. Extensive experiments comparing eight up-to-date baselines on two real-world datasets, Amazon and YelpChi, demonstrate the superiority of FRAUDRE.

A redundancy-based strategy is widely employed by assigning each task to multiple workers and then inferring the correct answer (called truth) for each task in crowdsourcing. Most existing truth inference methods are designed for the situation with a fairly big number of answers for each task (referred to as high redundancy). However, the high redundancy unavoidably leads to a high cost. In this work, we propose a novel truth inference approach called TWLR based on worker representations for the situation with a small number of answers for each task (referred to as low redundancy). We develop a deep model to learn the representations of workers considering both answers and worker-task relations. For each task, we identify the worker with the highest quality, and select his/her answer as the predicted answer. To the best of our knowledge, this is the first work to perform truth inference by utilizing deep learning techniques to deal with the low redundancy situation in crowdsourcing. We have conducted a set of experiments against 7 real-world datasets to show the accuracy improvement of our truth inference approach by comparing with 11 baseline methods.

Crowdsourcing platforms are widely used for requesters to find workers for general tasks. The answers to general tasks are usually open and not constrained by multiple choices. For the general tasks, the worker performance prediction models can facilitate the task assignment process in crowdsourcing. Worker performance prediction is affected by the three roles: the worker, the requester, and the task. The existing worker performance prediction models mainly consider the features of tasks and workers. However, these models rarely consider the features of requesters. And the existing worker performance prediction models for multiple-choice tasks are not suitable for general tasks as they are built based on the workers' accuracy on choices. In this work, we propose a worker performance prediction model by taking account of features of workers, tasks, and requesters to help requesters select workers for their general tasks on crowdsourcing platforms. We design a relationship learning module to learn the low dimension relationship representations of workers, tasks, and requesters. Furthermore, we design a performance learning model to predict workers' performance based on the features and relationship representations of workers, tasks, and requesters. A set of experiments against the realworld dataset from the Zhubajie platform has been conducted. Experimental results show that the proposed approach has better prediction results than the existing baseline methods.

Ridesharing is a challenging topic in the urban computing paradigm, which utilizes urban sensors to generate a wealth of benefits and thus is an important branch in ubiquitous computing. Traditionally, ridesharing is achieved by mainly considering the received user ridesharing requests and then returns solutions to users. However, there lack research efforts of examining user acceptance to the proposed solutions. To our knowledge, user decisions in accepting/rejecting a rideshare is one of the crucial, yet not well studied, factors in the context of dynamic ridesharing. Moreover, existing research attention is mainly paid to find the nearest taxi, whilst in reality the nearest taxi may not be the optimal answer. In this paper, we tackle the above un-addressed issues while preserving the scalability of the system. We present a scalable framework, namely TRIPS, which supports the probability of accepting each request by the companion passengers and minimizes users' efforts. In TRIPS, we propose three search techniques to increase the efficiency of the proposed ridesharing service. We also reformulate the criteria for searching and ranking ridesharing alternatives and propose indexing techniques to optimize the process. Our approach is validated using a real, large-scale dataset of 10,357 GPS-equipped taxis in the city of Beijing, China and showcases its effectiveness on the ridesharing task.

Vehicular ad hoc networks ( VANETs ) and the services they support are an essential part of intelligent transportation. Through physical technologies, applications, protocols, and standards, they help to ensure traffic moves efficiently and vehicles operate safely. This article surveys the current state of play in VANETs development. The summarized and classified include the key technologies critical to the field, the resource-management and safety applications needed for smooth operations, the communications and data transmission protocols that support networking, and the theoretical and environmental constructs underpinning research and development, such as graph neural networks and the Internet of Things. Additionally, we identify and discuss several challenges facing VANETs, including poor safety, poor reliability, non-uniform standards, and low intelligence levels. Finally, we touch on hot technologies and techniques, such as reinforcement learning and 5G communications, to provide an outlook for the future of intelligent transportation systems.

The merging boundaries between edge computing and deep learning are forging a new blueprint for the Internet of Things (IoT). However, the low-quality of data in many IoT platforms, especially those composed of heterogeneous devices, is hindering the development of high-quality applications for those platforms. The solution presented in this article is intelligent data collaboration, i.e., the concept of deep learning providing IoT with the ability to adaptively collaborate to accomplish a task. Here, we outline the concept of intelligent data collaboration in detail and present a mathematical model in general form. To demonstrate one possible case where intelligent data collaboration would be useful, we prepared an implementation called adaptive data cleaning (ADC), designed to filter noisy data out of temperature readings in an IoT base station network. ADC primarily consists of a denoising autoencoder LSTM for predictions and a four-level data processing mechanism to perform the filtering. Comparisons between ADC and a maximum slop method show ADC with the lowest false error and the best filtering rates.

Trust relationship prediction among people provides valuable supports for decision making, information dissemination, and product promotion in online social networks. Network embedding has achieved promising performance for link prediction by learning node representations that encode intrinsic network structures. However, most of the existing network embedding solutions cannot effectively capture the properties of a trust network that has directed edges and nodes with in/out links. Furthermore, there usually exist rich user attributes in trust networks, such as ratings, reviews, and the rated/reviewed items, which may exert significant impacts on the formation of trust relationships. It is still lacking a network embedding-based method that can adequately integrate these properties for trust prediction. In this work, we develop an AtNE-Trust model to address these issues. We firstly capture user embedding from both the trust network structures and user attributes. Then we design a deep multi-view representation learning module to further mine and fuse the obtained user embedding. Finally, a trust evaluation module is developed to predict the trust relationships between users. Representation learning and trust evaluation are optimized together to capture high-quality user embedding and make accurate predictions simultaneously. A set of experiments against the real-world datasets demonstrates the effectiveness of the proposed approach.

General tasks on crowdsourcing platforms attract more and more workers with different skills and experiences. Existing approaches only leverage the information from tasks with feedback to evaluate worker ability. However, there are millions of tasks without feedback on the platforms. The participating behavior of workers involved in these tasks has not been exploited. In this work, we propose a worker ability model PB-Worker to support general tasks on crowdsourcing platforms. We model the worker latent relation and task latent relation by exploiting the worker participating behavior. To the best of our knowledge, this is the first work to consider the worker participating behavior. Our model is a semi-supervised model that can cover tasks with feedback and tasks without feedback. We employ the ladder network to generate the representations of workers and employ the neural network to predict the worker ability scores. A set of experiments against the real-world dataset from the Zhubajie platform has been conducted. Experimental results show that the output quality of the proposed approach is better than the existing baseline methods.

As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics. However, the classic methods of community detection, such as spectral clustering and statistical inference, are falling by the wayside as deep learning techniques demonstrate an increasing capacity to handle high-dimensional graph data with impressive performance. Thus, a survey of current progress in community detection through deep learning is timely. Structured into three broad research streams in this domain – deep neural networks, deep graph embedding, and graph neural networks, this article summarizes the contributions of the various frameworks, models, and algorithms in each stream along with the current challenges that remain unsolved and the future research opportunities yet to be explored.

With the rising demand of smart mobility, ride-hailing service is getting popular in the urban regions. These services maintain a system for serving the incoming trip requests by dispatching available vehicles to the pickup points. As the process should be socially and economically profitable, the task of vehicle dispatching is highly challenging, specially due to the time-varying travel demands and traffic conditions. Due to the uneven distribution of travel demands, many idle vehicles could be generated during the operation in different subareas. Most of the existing works on vehicle dispatching system, designed static relocation centers to relocate idle vehicles. However, as traffic conditions and demand distribution dynamically change over time, the static solution can not fit the evolving situations. In this paper, we propose a dynamic future demand aware vehicle dispatching system. It can dynamically search the relocation centers considering both travel demand and traffic conditions. We evaluate the system on real-world dataset, and compare with the existing state-of-the-art methods in our experiments in terms of several standard evaluation metrics and operation time. Through our experiments, we demonstrate that the proposed system significantly improves the serving ratio and with a very small increase in operation cost.

Enabling the analysis of behavioral disorders over time in social networks, can help in suicide prevention, (school) bullying detection and extremist/criminal activity prediction. In this paper, we present a novel data analytics pipeline to enable the analysis of patterns of behavioral disorders on social networks. We present a Social Behavior Graph (sbGraph) model, to enable the analysis of factors that are driving behavior disorders over time. We use the golden standards in personality, behavior and attitude to build a domain specific Knowledge Base (KB). We use this domain knowledge to design cognitive services to automatically contextualize the raw social data and to prepare them for behavioral analytics. Then we introduce a pattern-based word embedding technique, namely personality2vec, on each feature extracted to build the sbGraph. The goal is to use mathematical embedding from a space with a dimension per feature to a continuous vector space which can be mapped to classes of behavioral disorders (such as cyber-bullying and radicalization) in the domain specific KB. We implement an interactive dashboard to enable social network analysts to analyze and understand the patterns of behavioral disorders over time. We focus on a motivating scenario in Australian government's office of the e-Safety commissioner, where the goal is to empowering all citizens to have safer, more positive experiences online.

Truth discovery has been widely studied in recent years as a fundamental means for resolving the conflicts in multi-source data. Although many truth discovery methods have been proposed based on different considerations and intuitions, investigations show that no single method consistently outperforms the others. To select the right truth discovery method for a specific application scenario, it becomes essential to evaluate and compare the performance of different methods. A drawback of current research efforts is that they commonly assume the availability of certain ground truth for the evaluation of methods. However, the ground truth may be very limited or even impossible to obtain, rendering the evaluation biased. In this article, we present CompTruthHyp , a generic approach for comparing the performance of truth discovery methods without using ground truth. In particular, our approach calculates the probability of observations in a dataset based on the output of different methods. The probability is then ranked to reflect the performance of these methods. We review and compare 12 representative truth discovery methods and consider both single-valued and multi-valued objects. The empirical studies on both real-world and synthetic datasets demonstrate the effectiveness of our approach for comparing truth discovery methods.

User Interfaces (UI) are the bridge to connect Business Processes (BPs) and end users. The implementation of UIs normally needs a lot of manual efforts of developers. Aiming to resolve this issue, this work proposes a UI derivation method with a role-enriched BP (REBP) model as its foundation. This process model has the capability to present the details of task control flow and data operations in tasks. A set of control flow patterns and data operation patterns is identified. For each participant role, tasks of a process are abstracted and aggregated, then data relationships are extracted according to the identified control flow patterns and data operation patterns. A set of mandatory and recommended rules has been developed for deriving the UI logic from a BP. The solution for the UI derivation has been provided and implemented in the prototype. This proposed UI derivation method can provide help for the analysis, design, and maintenance of UI components of BPs.

The overwhelming amount of information continuously flowing through the Twitter environment makes topic derivation essential. It indeed plays a valuable role in a variety of Twitter-based applications, including content recommendations, news summarization, market analysis, etc. Topic derivation methods are typically based on semantic features of tweet contents. Because tweets are short by nature, such methods suffer from data sparsity. To alleviate this problem, this paper proposes a topic derivation method that incorporates tweet text similarity and interactions measures. Besides the tweet contents, the approach takes into account several types of interactions amongst tweets: Tweets which mention the same people, replies and retweets. Topic derivation is done through a two-step matrix factorization process. We conducted a number of experiments on several Twitter datasets to reveal both the individual and integrated effects of the various features being considered. Our experimental results against TREC2014 and our self collected tweetMarch datasets demonstrate that the proposed method is able to provide more than 30 percent improvement compared to other advanced topic derivation methods.

Trust prediction in online social networks is crucial for information dissemination, product promotion, and decision making. Existing work on trust prediction mainly utilizes the network structure or the low-rank approximation of a trust network. These approaches can suffer from the problem of data sparsity and prediction accuracy. Inspired by the homophily theory, which shows a pervasive feature of social and economic networks that trust relations tend to be developed among similar people, we propose a novel deep user model for trust prediction based on user similarity measurement. It is a comprehensive data sparsity insensitive model that combines a user review behavior and the item characteristics that this user is interested in. With this user model, we firstly generate a user's latent features mined from user review behavior and the item properties that the user cares. Then we develop a pair-wise deep neural network to further learn and represent these user features. Finally, we measure the trust relations between a pair of people by calculating the user feature vector cosine similarity. Extensive experiments are conducted on two real-world datasets, which demonstrate the superior performance of the proposed approach over the representative baseline works.

Developing effective network embedding methods for social trust networks (STNs) is a non-trivial problem because two key pieces of information need to be preserved simultaneously: a user's relations to latent factors and the trust transfer patterns that govern what type of relationship will form. In this study, we propose a novel social trust network embedding method (STNE) to address these issues. Specifically, we present a modified Skip-Gram model with negative sampling to jointly learn latent factor features, along with the trust transfer pattern features. Moreover, we define a flexible notion about a user's latent relationships with other users, which generates reliable negative samples for optimization. Extensive experiments on several real-world networks demonstrate the efficacy of the proposed STNE.

Trust evaluation of people and information on Twitter is critical for maintaining a healthy online social environment. How to evaluate the trustworthiness of users and tweets becomes a challenging question. In this demo, we show how our proposed CoTrRank approach deal with this problem. This approach models users and tweets in two coupled networks and calculate their trust values in different trust spaces. In particular, our solution provides a configurable way when mapping the calculated raw evidences to the trust values. The CoTrRank demo system has an interactive interface to show how our proposed approach produces more effective and adaptive trust evaluation results comparing with baseline methods.

Evolutionary clustering is a way of detecting the evolving patterns of communities in dynamic social networks. In principle, the detection process seeks to simultaneously maximize clustering accuracy at the current time step and minimize the clustering drift between two successive time steps. Several evolutionary clustering methods have been developed in an attempt to find the best trade-off between clustering accuracy and temporal smoothness, but the classic genetic operators in these methods do not make the best of the inter- and intra-connected relationships between nodes, which limits their effectiveness. To overcome this problem, we propose a novel migration operator to work in tandem with classic genetic operators to improve the discovery of evolving community structures. The operator is implemented within an existing genetic algorithm which relies on a genome representation under a decomposition framework that formulates evolutionary community detection as a multiobjective optimization problem. Moreover, we present a new method of calculating modularity directly from a genome matrix as the objective for measuring the snapshot quality, which results in a wider search space for finding the optimal solution. Experimental results over several synthetic networks and one real-world dynamic social network suggest that our method is superior to two other state-of-the-art methods in terms of both accuracy and smoothness in discovering evolving community structures in dynamic social networks.

Business processes are designed to achieve business goals under procedural rules by orchestrating tasks, information and documents. Managing data inconsistency in business processes is a challenging task. If not managed properly, business will face negative financial consequences. From literatures, BPMN modelling approaches deal with inconsistency problem by patterns; data provenance approaches analyze data generated in business process and investigate the reachability between data points. Although substantial works have been done, the data inconsistency problem has not been properly resolved. In particular, it is still lacking of modelling language and resolution for inconsistency caused by multiple starting points of business processes and dynamics of business processes execution. This paper provides data consistency solution in two aspects: a business process modelling in enriched business workflow notation with data states and temporal properties, and a workflow query algorithm to discovery data inconsistency issue.

Unlabeled, multi-view data presents a considerable challenge in many real-world data analysis tasks. These data are worth exploring because they often contain complementary information that improves the quality of the analysis results. Clustering with multi-view data is a particularly challenging problem as revealing the complex data structures between many feature spaces demands discriminative features that are specific to the task and, when too few of these features are present, performance suffers. Extreme learning machines (ELMs) are an emerging form of learning model that have shown an outstanding representation ability and superior performance in a range of different learning tasks. Motivated by the promise of this advancement, we have developed a novel multi-view fusion clustering framework based on an ELM, called MVEC. MVEC learns the embeddings from each view of the data via the ELM network, then constructs a single unified embedding according to the correlations and dependencies between each embedding and automatically weighting the contribution of each. This process exposes the underlying clustering structures embedded within multi-view data with a high degree of accuracy. A simple yet efficient solution is also provided to solve the optimization problem within MVEC. Experiments and comparisons on eight different benchmarks from different domains confirm MVEC’s clustering accuracy.

Social recommendation incorporates the social information of the user, such as friend relationship and trust relationship into the traditional recommendation system. From this point of view, social recommendation expands the function of the traditional recommendation to some extent. However, the existing social recommendation methods mostly focus on the general social relation between users and neglects the refinement of group information based on individual interest and trust. To this end, this paper proposes a novel social recommendation model based on the collective intelligence awareness driven by individual interest and trust. Experiments on two real-world datasets demonstrate that the proposed social recommendation method based on group information and individual feature outperforms the three baseline methods on the two evaluation metrics MAE and RMSE.

Conceptual elevation of data in business process modeling was first formulated in 2003. The research community responded to this new idea enthusiastically. In the past decade, there have been numerous research activities concerning the interactions between business processes/activities and data in many aspects of business process management. Many of the advancements have or will have impacted on design/modeling, analysis, implementation of business processes in practice. However, there is still a lack of techniques for developing a suite of "interrelated" processes realizing a business service. Interrelated processes are a cluster of processes that share data and other resources, are collectively constrained by regulations and policies, influence KPIs as a group. Many current practical applications of business workflow systems are in urgent need for tools and techniques for process clusters. In this paper, we formulate a broad notion of an "Enterprise Process Framework" (EPF) to address this need and outline several interesting research challenges arising from EPFs.

When Influence Maximization (IM) is applied to social network to maximise the network coverage, it becomes an effective mechanism for marketing applications. In this paper, we focus on a specific influence maximization problem, i.e., selecting a set of seeds on twitter to maximise information propagation, which can be used for information reaching out in marketing campaigns. The proposed approach is taking into the consideration of social ties, user interactions, and information propagation on Twitter. The influence probability is calculated according to users' action history including tweet, favourite, mention/reply, and retweet. An information diffusion model is proposed with the capability to simulate the dynamic process of information spread on Twitter. A concise heuristic algorithm is developed for influence maximization accordingly. Experimental results and analysis are provided based on a real Twitter network including 3,292 users in Darwin city in Australia.

Interactions among tweets, i.e., mentions, retweets, replies, are important factors contributing to the quality of topic derivation on Twitter. If applied correctly, the incorporation of tweet interactions can significantly improve the quality of topic derivation in comparison with approaches that are mainly based on the content similarity analysis. However, how interactions can be measured and integrated with content similarity for topic derivation remains a challenge. In previous work, the strength of tweet-to-tweet relationship has been computed by simply adding measures for content similarity, mentions, and reply-retweets. This simple linear addition does not accurately reflect the various impacts these factors have on tweet relationships. In order to address this issue, we propose a joint probability model that can effectively integrate the effects of the content similarity, mentions, and reply-retweets to measure the tweet relationship for the purpose of topic derivation. The proposed method is based on matrix factorization techniques, which enables a flexible implementation on a distributed system in an incremental manner. Experimental results show that the proposed model results in a significant improvement in the quality of topic derivation over existing methods.

The task abstraction and aggregation in a business process can help to (1) obtain customized descriptions of a business process for different users, and (2) derive user interfaces of a business process related to the participating users. This paper proposes an approach for task abstraction and aggregation of a business process based on a role-enriched business process model. For each user role, tasks of a business process are abstracted and aggregated according to the identified control flow patterns. A set of elementary operations for task abstraction and aggregation in business process are specified. The algorithm for deriving the abstracted and aggregated business process is developed. The structural consistency between the business process and the abstracted and aggregated business process has been analyzed. The derived abstracted and aggregated business processes with the proposed approach can be used to support analysing, developing, and updating software components such as user interfaces related to different user roles.

To keep up with the trend of globalization and informatization, an increasing number of enterprises decide to run their business process in a service-based manner with the help of Web Service technology. In order to manage such service-based business process (SBP), it is vital that the dependencies among the internal process and the exposed external services are correctly developed and maintained. SBP is dynamic by nature, therefore it is necessary to develop a practical and robust method to verify the correctness of SBP. In an SBP, complex dependencies exist not only between internal process and involved services but also within their components (activities, data, operations, etc.). The complex dependencies make the correctness verification for SBP a challenging task. In this work, we develop a correctness verification approach to handle this task. A Petri net based model is proposed with a hierarchical structure to cover the characteristics of SBPs. This model can support the control flow patterns that are necessary for SBPs. A set of correctness properties for SBP are identified which any SBP developers shall consider, and the respective verification methods are developed.

Summary Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on‐demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first‐class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy‐aware manner. In this paper, we propose an energy‐aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method. Copyright © 2016 John Wiley & Sons, Ltd.

In this article, we propose a comprehensive approach for Quality of Service (QoS) calculation in service composition. Differing from the existing work on QoS aggregations that represent QoS as single values, discrete values with frequencies, or standard statistical distributions, the proposed approach has the capability to handle any type of QoS probability distribution. A set of formulae and algorithms are developed to calculate the QoS of a composite service according to four identified basic patterns as sequential, parallel, conditional, and loop. We demonstrate that the proposed QoS calculation method is much more efficient than existing simulation methods. It has a high scalability and builds a solid foundation for real-time QoS analysis and prediction in service composition. Experiment results are provided to show the effectiveness and efficiency of the proposed method.

An important omission in current development practice for business process (or workflow) management systems is modeling of data & access for a business process, including relationship of the process data and the persistent data in the underlying enterprise database(s). This paper develops and studies a new approach to modeling data for business processes: representing data used by a process as a hierarchically structured business entity with (i) keys, local keys, and update constraints, and (ii) a set of data mapping rules defining exact correspondence between entity data values and values in the enterprise database. This paper makes the following technical contributions: (1) A data mapping language is formulated based on path expressions, and shown to coincide with a subclass of the schema mapping language Clio. (2) Two new notions are formulated: Updatability allows each update on a business entity (or database) to be translated to updates on the database (or resp. business entity), a fundamental requirement for process implementation. Isolation reflects that updates by one process execution do not alter data used by another running process. The property provides an important clue in process design. (3) Decision algorithms for updatability and isolation are presented, and they can be easily adapted for data mappings expressed in the subclass of Clio.

Quality of service (QoS) is a major concern in the design and management of a composite service. In this paper, a systematic approach is proposed to calculate QoS for composite services with complex structures, taking into consideration of the probability and conditions of each execution path. Four types of basic composition patterns for composite services are discussed: sequential, parallel, loop, and conditional patterns. In particular, QoS solutions are provided for unstructured conditional and loop patterns. We also show how QoS-based service selection can be conducted based on the proposed QoS calculation. Experiments have been conducted to show the effectiveness of the proposed method.

Business collaboration is about coordinating the flow of information among organizations and linking their business processes into a cohesive whole. Collaborative business processes are time critical within and across organizations and can become unreliable due to temporal inconsistency where processes cannot execute according to the agreed temporal policies. It is necessary to have a mechanism to manage temporal consistency in service-oriented business collaboration. In this paper, we propose a model named Timed Choreographical Business Transaction Net (TiCoBTx-Net) based on Hierarchical Colored Petri Net for individual business participants to specify and manage the temporal consistency in business collaboration. A series of temporal polices are formalized and checked in TiCoBTx-Net to enforce the temporal consistency at design time and runtime. A verification mechanism is also developed to clarify the status of temporal inconsistencies. Finally, the implementation details of the proposed mechanism is provided.

The increasing processing power, storage and support of multiple network interfaces are promising the mobile devices to host services and participate in service discovery network. A few efforts have been taken to facilitate provisioning mobile Web services. However they have not addressed the issue about how to host heavy-duty services on mobile devices with limited computing resources in terms of processing power and memory. In this paper, we propose a framework which partitions the workload of complex services in a distributed environment and keeps the Web service interfaces on mobile devices. The mobile device is the integration point with the support of backend nodes and other Web services. The functions which require the resources of the mobile device and interaction with the mobile user are executed locally. The framework provides support for hosting mobile Web services involving complex business processes by partitioning the tasks and delegating the heavy-duty tasks to remote servers. We have analyzed the proposed framework using a sample prototype. The experimental results have shown a significant performance improvement by deploying the proposed framework in hosting mobile Web services.

In this paper, we propose an approach to deal with the change management for service oriented business processes. Beyond existing work, the proposed approach highlights the dependencies between services and business processes. A service oriented business process model is devised for capturing the major characteristics of change management in service oriented context. The taxonomy for the changes associated with services and business processes is presented. A set of change impact patterns are specified and the functions for calculating impact scopes of a change are defined. With the help of the change taxonomy and the change impact patterns, the ripple effect of changes of the business processes and services can be clearly analyzed. This research provides a step progress for change management in the service oriented environment.

In this paper, a QoS Distribution estimation Tool (QoSDIST) is developed to estimate the QoS distributions for service compositions. QoSDIST can generate QoS probability distributions for component web services. When estimating the QoS probability distribution for a service composition, QoSDIST does not put any constraints on the representation of the QoSs of component web services, i.e., the QoS of a component web service can be in single value, discrete values with frequencies, standard statistical distribution, or any general distribution regardless of its shape, which can not be done by any existing approaches. Moreover, QoSDIST can deal with commonly used composition patterns, including loop with arbitrary exit points.

Trust and reputation for web services emerges as an important research issue in web service selection. Current web service trust models either do not integrate different important sources of trust (subjective and objective for example), or do not focus on satisfying different user's requirements about different quality of service (QoS) attributes such as performance, availability etc. In this paper, we propose a Bayesian network trust and reputation model for web services that can overcome such limitations by considering several factors when assessing web services' trust: direct opinion from the truster, user rating (subjective view) and QoS monitoring information (objective view). Our comprehensive approach also addresses the problems of users' preferences and multiple QoS-based trust by specifying different conditions for the Bayesian network and targets at building a reasonable credibility model for the raters of web services.

Introduction to the Special Issue with Best Papers from BPM 2008 WorkshopsThis special issue contains the best papers selected from the workshops that were held in conjunction with the 6th International Conference on Business Process Management (BPM 2008) that took place in Milan, Italy.The nine workshops included some important and challenging areas in business process management:• Business Process Management and Social Software (BPMS2) focuses on the interaction of social software and the underlying paradigm of social production with business processes.It explores how social software and social production interact with business process management, how business process management has to change to comply with social production, and how business processes may profit from social techniques.• Model-Driven Engineering for Business Process Management (MDE4BPM) is about the application of model-driven engineering to business process management.It focuses on research problems that arise when the model-driven engineering and development methodology is applied to automate the whole life cycle of business process modelling artifacts (e.g., automatically mapping high-level business process models to executable IT-level workflows).• Process Management for Highly Dynamic and Pervasive Scenarios (PM4HDPS) recognizes how nowadays process management systems are also being used in pervasive and highly dynamic situations, such as emergency management, pervasive healthcare and ambient intelligence.It requires novel approaches merging traditional BPM with artificial intelligence, agent programming and robotics.• QoS of Self-healing Web Services (QSWS) attacks the emerging area of Web service composition, QoS management and diagnosis.It focuses on distributed and centralized solutions for the diagnosis and repair of choreographed and/or orchestrated Web services, QoS management in choreographed and/or orchestrated Web services, ontologies for QoS.• Collaborative Business Processes (CBP) studies how the business processes of different organizations can be integrated seamlessly, while the constructed business process network dynamically evolves to adapt to the continuously changing business conditions.Nowadays organizations have been undergoing a thorough transformation towards highly flexible and agile collaborations.

Modern distributed systems require collaborations between individual organizations. The collaborations consist of complex relationships and interactions among distinct administrative domains. Different forms of collaboration can be implemented. As a result, different security control requirements emerge. For example, service propagation requires all collaborating partners to have compatible authorization policies. On the other hand, integratable policies are required for organizations who intend to engage in service composition. Therefore, a thorough understanding of different collaboration patterns is a prerequisite for secure inter-organizational collaboration. In this paper, we define basic inter-organizational collaboration patterns and propose Collaboration Policies that outline requirements for collaboration partners in each collaboration pattern in terms of authorization policy consistency.

Based on real development experience, the paper presents a collection of design techniques for building enterprise web services. By applying the techniques to web services development, not only the development increases reusability and productivity, but also the web services improve agility and compatibility.Enterprise web services require high grade of competency in designing web service contracts. A contract of web service formalizes an agreement between web service provider and consumer, in the forms of WSDLs, service schemas and policies. Though contract-first method provides great potential of directly dealing with the contracts, and a number of articles have been published regarding designing WS and XML schemas, however it is still hard for developers to find cookbooks or guidelines concentrated on designing web service contracts with contract-first method. To fill the gap, a set of design techniques are introduced and deployed in practice, incorporating some best practices scattered over the web services community. These techniques cover most of the key aspects of web service, including consolidating service schemas in line with business entities, constructing coarse-grained namespaces, applying versioning over WSDLs and service schemas, and writing fine-grained filters with contracts.

Presents the introductory welcome message from the conference proceedings.

Provides a listing of current committee members and society officers.

The adoption of Web service technologies in dynamic business applications such as on-demanding computing or highly reconfigurable virtual enterprises advocates for methods and tools for describing and managing non-functional aspects of services, e.g., the reliability, accessibility and accuracy of a service. These properties are referred as quality of service (QoS). Failing to accurately specify and communicate quality details can lead to wrong and inconsistent expectations held by the service provider and service consumer and result in serious disagreement. Popular Web service description development and management solutions, e.g. WS-Policy and Web Service Level Agreement, do not properly cater for the specification and negotiation of quality related requirements for collaborative services. In this paper we present a comprehensive mechanism for the specification of quality requirements. The proposed approach allows service providers and consumers to explicitly define and adjust both higher level and concrete measurement requirements for quality of a service in a formally verifiable manner. As a result, quality negotiations can be performed effectively for the establishment of service provision agreements.

The collaborative business process can be unreliable when business partners collaborate in a peer-to-peer fashion without central control. An important issue that needs to be dealt with for any generic solution to manage collaborative business transaction is reliability verification. In this paper, a business collaboration model, choreographical business transaction net (CoBTx-Net) is developed for individual business participants to specify and manage the collaboration. Three reliability properties named time-embedded dead marking freeness, inter-organizational dead marking freeness, and collaborative soundness are defined and exploited to verify reliability based on CoBTx-Net.

Service oriented computing (SOC) allows resources on a network to be made available as services. For a business service, differentiated services can be provided based on the usage context, i.e., location, age, purpose and user profiles. In differentiated services, service outcomes depend on context. Currently there is no efficient technical solution for supporting differentiated service development. In this paper we present an approach to deliver differentiated services realized by configurable business processes so that service flexibility, manageability and reusability can be achieved.

Nowadays in order to stay competitive, business needs to collaborate with each other to offer value added services. Collaborative business process development require loose coupling between individual business processes so that they can interoperate freely across the Internet and that the collaboration can be established in a highly dynamic fashion and on-demand basis. Service oriented computing (SOC) is able to meet such request by establishing business collaboration (BC) to utilize business processes (BPs) crossing organizational boundaries. BP is organizational based, which is about one organization composing services; while BC is choreographically based, which is about cooperation between organizations by linking their BPs. A successful BC development needs to follow the chorographical requirement to ensure the consistency among individual BPs across organizations. It also needs to meet the organizational requirements to ensure each organization's BP performs according to its business rules and conforms to what is specified from collaboration point of view. The current Web services (WS) technologies have provided specifications and tools for BC development. However methodologies, and real case study and experience report in BC development is lacking in the literature. In this paper a business collaboration framework (BCF) is proposed which breaks the BC requirements into 3 layers, each of which models different aspect in the BC development. Practical experience and issues on how to use the BCF is presented and discussed in a case studyeveloping BC in capital marketplace (CM), a project sponsored by the Capital Markets Cooperative Research Centre (CMCRC)

Current composite web service development and management solutions, e.g. BPEL, do not cater for assessing and maintaining comparability of business partners during business collaboration development and management. It is our firm belief that to realize the vision of developing business collaborations on demand and on the fly while ensuring that collaboration between participants involved can take place, business collaboration design needs to apply software development principles and at the same time incorporate support for specification of relationships among requirements and capabilities of different participants. In this paper we introduce the business collaboration design framework, which uses a blend of design perspectives, facets and aspects to provide designers with the means to develop and deliver business collaborations in an effective and comprehensive manner. We then explore how compatibility in collaborations designs can be handled in a verifiable manner by establishing a traceability mechanism, which facilitates assessment of the impact of change on the diverse aspects of business collaborations. Categories and Subject Descriptors D.2.1 [Software Engineering]: Requirements/Specification – languages, methodologies; D.2.4 [Software Engineering]: Software/Program verification – model checking, programming by

Web services are becoming the dominant paradigm for distributed computing and electronic business. This has raised the opportunity for service providers and application developers to create value added services by combining Web services. Several Web service composition solutions have been proposed, e.g. BPEL4WS. However, these approaches are either not flexible or too complicated as they lack proper support for modularity and reusability. Motivated by the demand of a light-weighted tool of service composition, we developed ServiceCom, a tool for service composition specification, construction and execution. In this paper we discuss how ServiceCom supports reusable Web service composition specification, combination, and execution.

Web services are becoming the prominent paradigm for distributed computing and electronic business. This has raised the opportunity for service providers and application developers to develop value-added services by combining existing Web services. However the current Web service composition solutions, even for the applications developed on the basis of the standard Business Process Execution Language for Web Services (BPEL for short), are rather restricted and inflexible as they lack proper support for generating dynamic compositions and for managing the service composition life cycle. The ReServCom project proposed here aims to remedy this situation by introducing a rule based approach for Web service composition which combines best practices from rule base systems and software engineering to support parameterization, dynamic binding, and flexible service compositions.

A data warehouse (DW) contains multiple views accessed by queries. One of the most important decisions in designing a DW is selecting views to materialize for the purpose of efficiently supporting decision making. The search space for possible materialized views is exponentially large. Therefore heuristics have been used to search for a near optimal solution. In this paper, we explore the use of an evolutionary algorithm for materialized view selection based on multiple global processing plans for queries. We apply a hybrid evolutionary algorithm to solve three related problems. The first is to optimize queries. The second is to choose the best global processing plan from multiple global processing plans. The third is to select materialized views from a given global processing plan. Our experiment shows that the hybrid evolutionary algorithm delivers better performance than either the evolutionary algorithm or heuristics used alone in terms of the minimal query and maintenance cost and the evaluation cost to obtain the minimal cost.

The Internet now makes it possible for people to build complex applications by combining data and processing offered by different information systems (services) available across the network. These services normally have very diverse data structure and processing capabilities. Query processing in this kind of environment is a difficult problem due to the wide range of capabilities of the services. We believe to have a proper way to describe the structure, functionalities, and capabilities of the services is the first step towards generating feasible and efficient query plan(s) for the underlying service to process. We propose a framework for service description.

A data warehouse contains multiple views accessed by queries. One of the most important decisions in designing a data warehouse is the selection of materialized views for the purpose of efficiently implementing decision making. The search space for the selection of materialized views is exponentially large, therefore, heuristics have been used to search a small fraction of the space to get a near optimal solution. In this paper, we explore the use of a genetic algorithm for the selection of materialized views based on multiple global processing plans for many queries. Our experimental studies indicate that the genetic algorithm delivers better solutions than some heuristics.

As a sufficiently abstract level, the data in the data warehouse can be seen as a set of materialized views, where the base data resides at the information sources. These materialized views are designed based on the users' requirements (e.g., frequently asked queries). However, a data warehouse is a dynamic environment, i.e., when user query requirement changes, the existing materialized views should evolve to meet the new requirement. These changes will demand schema changes at the warehouse and should be handled with as little disruption or modification to other components of the warehousing system as possible. We propose a framework to determine if the existing materialized views will be affected by the requirement changes, and how they are affected. Algorithms are proposed to deal with the situation when a new query is added in. The aim of the algorithms is to efficiently get the new materialized views by analysing the relationships among queries using MVPP, a specification for a query processing plan.

Data warehouses are accessed by different queries with different frequencies. The portions of data accessed by a query can be treated as a view. When these views are related to each other and defined over overlapping portions of the base data, then it may be more efficient not to materialize all the views, but rather to materialize certain "shared views" from which the query results can be generated. We address some issues related to determining this set of shared views to be materialized in order to achieve the best combination of good performance and low maintenance, and provide an algorithm for achieving this goal.

Article Free Access Share on A configurable approach for object sharing among multidatabase systems Authors: Jian Yang Dept of Computer Science, University College, UNSW Australian Defence Force Academy, Canberra ACT 2600, Australia Dept of Computer Science, University College, UNSW Australian Defence Force Academy, Canberra ACT 2600, AustraliaView Profile , Mike P. Papazoglou School of Information Systems, Faculty of Information Technology, Queensland University of Technology, Brisbane QLD 4001, Australia School of Information Systems, Faculty of Information Technology, Queensland University of Technology, Brisbane QLD 4001, AustraliaView Profile Authors Info & Claims CIKM '95: Proceedings of the fourth international conference on Information and knowledge managementDecember 1995 Pages 129–136https://doi.org/10.1145/221270.221358Online:02 December 1995Publication History 1citation82DownloadsMetricsTotal Citations1Total Downloads82Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF