Community search is a fundamental graph-based retrieval problem that aims to identify a query-dependent subgraph whose nodes exhibit strong internal connectivity. While recent learning-based methods improve retrieval effectiveness via graph representation learning, they follow a ''one-use-one-train'' paradigm that requires retraining or fine-tuning for each target graph, leading to high data dependency, high training costs, and limited generalization. To handle this, we propose OFA-CS, a ''one-for-all'' community search framework trained once on source datasets and directly deployed to arbitrary unseen graphs without retraining or fine-tuning, while preserving strong performance. Specifically, we introduce a Spectral-Aware Feature Alignment module to unify feature dimensionality and align cross-domain semantics in a community-aware manner. We further develop a Graph Diffusion Tokenized Transformer that constructs hybrid token sequences from local and global structural contexts for Transformer encoding, and applies diffusion-based refinement to mitigate distribution shifts on unseen graphs. With the unified representations, communities are efficiently retrieved via a modularity-driven search procedure. Extensive experiments on diverse real-world graphs demonstrate that OFA-CS achieves strong cross-domain generalization and competitive retrieval effectiveness against state-of-the-art methods, without requiring target-domain supervision.

Temporal Knowledge Graph (TKG) reasoning seeks to predict future events by analyzing historical data, where the effective leverage of both local and global historical facts proves crucial. Existing approaches employ graph neural networks (GNNs) and recurrent neural networks (RNNs) for local evolution patterns, complemented by statistical methods to enhance attention to global facts, demonstrating efficient predictive capabilities. However, traditional GNNs, constrained by their low-order neighborhood aggregation design, inherently fail to model potential high-order dependencies among facts. Furthermore, existing global history modeling approaches may introduce irrelevant historical information that interferes with prediction tasks. To address these limitations, we propose a Dual History-aware HyperGraph Network for TKG reasoning, namely DHHGN. Specifically, for local history modeling, we design a hybrid hypergraph-graph joint recurrent convolution module that simultaneously captures low-order neighborhood information and high-order interaction patterns among entities, employing a gating mechanism to adaptively blend their contributions. For global history modeling, we propose a dual history enhancement module that amplifies attention on pivotal historical facts while ensuring holistic integration of all historical contexts. Extensive experiments on four public benchmarks validate that DualHist-HGN consistently outperforms existing state-of-the-art methods across TKG reasoning tasks.

ABSTRACT To celebrate the 50th Anniversary of the Information Systems Journal, we are delighted to share our research reflections on the article “Community-diversified influence maximization in social networks” published at Information Systems in 2020. Our reflections will highlight the impact of this article on the authors’ research trajectories, its influence on the broader research community, and its contributions to industry practice.

Learned indexes leverage machine learning models to approximate data distributions and predict key positions, offering better performance than traditional index structures such as B+Trees. As data in real-world applications evolve rapidly, the timely and efficient updating of learned indexes has become an increasingly important research problem, attracting growing attention in recent studies. However, under update-intensive workloads with frequent insertions and deletions, existing learned indexes cannot update the model in a timely manner. Moreover, they ignore the impact of deletions on model accuracy. These limitations lead to degraded prediction accuracy and increased query latency, undermining the core advantage of learned indexes. To fill in this gap, in this paper, we design a dynamic learned index (denoted as DynaMind) that is able to timely update the model with the frequent change of data. Specifically, we propose a novel score function to determine the appropriate timing at which a learned index should initiate an update by measuring the influence of updated data on the model accuracy. To enable efficient model updates, we devise a timely learned index update algorithm that implements both lightweight incremental learning for insertions and machine unlearning for deletions together, ensuring the model continuously evolves without full retraining. Extensive experiments on real-world and synthetic datasets show that DynaMind achieves competitive throughput compared to state-of-the-art works while improving the prediction accuracy. The proportion of keys with zero prediction error increases by more than 10% after updates.

Recommender systems (RSs) are widely adopted due to their effectiveness in modeling user preferences and generating personalized recommendations. However, data poisoning attacks (PAs) manipulate recommendation results by injecting fake user profiles, thereby affecting the quality and accuracy of RSs. Moreover, emerging high-concealed PAs (HCPAs) achieve greater evasion of detection by controlling the cost of the attack, simulating the behavior patterns of benign users, and carrying out the attack with less prior knowledge. The HCPAs bring challenges: (1) the very low cost of attacks not only leads to an imbalance in data distribution but also introduces a large amount of accidental co-occurrence noise; (2) the behavioral patterns similar to benign users make it difficult to describe the characteristics of HCPAs; and (3) the prior knowledge for detecting HCPAs in real scenarios is very limited. To address these challenges, we propose STOP, an orthogonal projection bi-hypersphere detection method built on multi-view relational disentanglement and information-consistent fusion. First, we model the distributional preferences of user ratings to eliminate rating and popularity bias, and construct a co-occurrence association graph to suppress accidental overlaps. To address data imbalance caused by HCPAs, second, we introduce a distributional-consensus importance screening method that filters out benign users weakly associated with potential attackers. To address the issues of noise and the difficulty in feature characterization, third, we propose a multi-view relational disentanglement and information-consistent fusion method, which can eliminate redundant relationships, separate key relations into sequence-varying and sequence-stable components over rating sequences, and retain task-related relationships. Finally, inspired by the “convergence theorem”, we design an orthogonal projection bi-hypersphere boundary learning detection method to reduce the high false alarm rate (FAR). We extensively evaluate STOP under various HCPA scenarios, demonstrating its superiority over existing methods with an average 12.34% improvement in detection rate and an average 2.75% reduction in FAR. Furthermore, forensic analysis on real-world unlabeled data reveals distinct attacker “fingerprints”, such as extreme ratings, contradictory review styles, and analysis of target items, validating STOP's reliability in practical applications.

Recommender systems (RSs), as crucial components of online services, can help users efficiently obtain information they may like. In reality, RSs face long-term threats. Attackers manipulate recommendation results by injecting malicious data in order to obtain benefits. At present, research on the security of RSs lacks a comprehensive understanding of attack capabilities. Moreover, existing defense strategies have not yet been systematically associated with attack characteristics. More importantly, existing defense methods rarely focus on real unlabeled data in practical application scenarios for anomaly detection and forensics. Therefore, this survey systematically analyzes the security of RSs and provides new insights. Specifically, we first categorize attack models from an attack perspective into: attack strategies based on targets, attack strategies against security and privacy, attack strategies based on prior knowledge, and attack strategies against other RSs. From a perspective of defense, existing detection models, second, can be divided into: behavioral representation based on statistics, detection based on hidden features, detection against privacy attacks, anomaly discovery based on association mining, and abnormality forensics for real-world data. Finally, we propose several potential research directions aimed at providing guidance for the security research of RSs. Additionally, to facilitate experimental reproducibility and comparative research, this survey also provides a repository of resources for attacks and defenses (https://github.com/xiaofengbbb/RS-Papers).

Language and binocular vision play a crucial role in human understanding of the world. Advancements in artificial intelligence have also made it possible for machines to develop 3D perception capabilities essential for high-level scene understanding. However, only monocular cameras are often available in practice due to cost and space constraints. Enabling machines to achieve accurate 3D understanding from a monocular view is practical but presents significant challenges. We introduce MonoMulti-3DVG, a novel task aimed at achieving multi-object 3D Visual Grounding (3DVG) based on monocular RGB images, allowing machines to better understand and interact with the 3D world. To this end, we construct a large-scale benchmark dataset, MonoMulti3D-ROPE, and propose a model, CyclopsNet that integrates a State-Prompt Visual Encoder (SPVE) module with a Denoising Alignment Fusion (DAF) module to achieve robust multi-modal semantic alignment and fusion. This leads to more stable and robust multi-modal joint representations for downstream tasks. Experimental results show that our method significantly outperforms existing techniques on the MonoMulti3D-ROPE dataset. Our dataset and code are available at https://github.com/JasonHuang516/MonoMulti-3DVG

The geological environment remote sensing interpretation task imposes higher demands on deep learning models' computational efficiency and deployment performance. As an efficient lightweight strategy, channel pruning has been widely applied in computer vision. However, when applied to geological remote sensing tasks, existing pruning methods struggle to balance interpretation accuracy and computational cost effectively, mainly due to the complexity of geological environment data and the constraints of resource-limited deployment. Moreover, although reinforcement learning-based pruning strategies have been proposed to overcome these limitations, they often suffer from limited exploration capability and unstable training performance. To address these issues, we propose a dynamic pruning method (SACRL) incorporating the maximum entropy mechanism, which enhances the explorability and stability of the pruning strategy by introducing an entropy regularity term in the reinforcement learning objective function. Experiments demonstrate that, compared to conventional pruning methods such as AMC and Taylor, SACRL demonstrates better performance in terms of overall classification accuracy (OA), mean Intersection over Union (mIoU), and mean Average Precision (mAP). Furthermore, deploying the pruned model on resourcelimited devices, such as the Jetson AGX Orin, further validates its adaptability in real-world applications and highlights its potential in remote sensing interpretation of geological environments.

Hybrid-storage blockchains have become a promising paradigm for scaling up query processing services. This paradigm provides a hybrid on/off-chain approach, where only small metadata is stored on-chain while the raw data is outsourced to off-chain storage. The key concerns for query processing in such a system are security and query integrity. However, existing schemes mostly address an impractical honest-but-curious model, rather than a more realistic malicious model, due to the intricate interplay between security and query integrity. Additionally, supporting efficient dynamic update with forward security is a significant challenge for blockchain systems. To address these issues, in this paper, we first formally define the problem of secure, reliably verifiable, and dynamic Boolean range queries in hybrid-storage blockchains (SRVF). Then, we propose a novel index called Virtual Keyword Forest (VKF), which achieves secure and reliably verifiable query processing in hybrid-storage blockchains. To support dynamic queries with forward security, we propose an adaptive version-control update scheme to integrate into VKF. Furthermore, to reduce communication overhead and gas consumption, we design an aggregation signature algorithm that ensures the verification information is of a fixed size. We provide theoretical security analysis and empirical evaluations, comparing our proposed approaches with the state-of-the-art approaches, to demonstrate their feasibility.

A corpus of recent work has revealed that authenticated query services have been under the spotlight due to the untrustworthiness of outsourced service provider. To enrich scalable functionality, there is an increasing demand for dynamically authenticated query. However, when implementing query and update simultaneously, traditional approaches heavily suffer from the inconsistency between verification digest and requested index and therefore are infeasible in reality. Moreover, the efficiency of storage, query, verification, and update is still a huge hinder when processing large scale data. To address these challenging issues, in this paper, we propose a novel idea of authenticated learned index that is carefully designed and actively optimized for authenticated query processing. Specifically, we first propose a version control update mechanism for consistency guarantee by maintaining historical index versions. Following this, we propose two basic authenticated learned indexes, i.e., query-friendly PVL-tree and update-friendly PVLB-tree, to support efficient scalable authenticated range query. Furthermore, to improve the efficiency, we introduce a hybrid index framework HPVL-tree based on two basic indexes. Extensive theoretical and experimental analysis demonstrate that our proposed HPVL-tree outperforms the state-of-the-art approaches by up to$2.28\times, 3.96\times$, and$2.51\times$in search time, update time, and verification time, respectively. Moreover, the storage overhead and communication overhead occupy only 38 % and 2.25 % of existing approach, respectively.

Knowledge Graphs (KGs) have revolutionized structured knowledge representation, yet their capacity to model real-world complexity and heterogeneity remains fundamentally constrained. The emerging paradigm of Multi-View Knowledge Graphs (MVKGs) addresses this gap through multi-view learning, but existing research lacks systematic integration. This survey provides the first systematic consolidation of MVKG methodologies, with four pivotal contributions: 1) The first unified taxonomy of view generation paradigms that rigorously categorizes view into four types: structure, semantic, representation, and knowledge & modality; 2) A novel methodological typology for view fusion that systematically classifies techniques by fusion targets (feature, decision, and hybrid); 3) Task-centric application mapping that bridges theoretical MVKG constructs to node/link/graph-level downstream tasks; 4) A forward-looking roadmap identifying underexplored challenges. By unifying fragmented methodologies and formalizing MVKG design principles, this survey serves as a roadmap for advancing KG versatility in complex AI-driven scenarios. In doing so, it paves the way for more efficient knowledge integration, enhanced decision-making, and cross-domain learning in real-world applications.

Deep neural networks on cloud platforms face growing security threats, with AI services increasingly relying on heterogeneous models for the same task to meet diverse user needs. Existing methods fail to distinguish benign modifications from malicious attacks in cross-model scenarios. To address this challenge, we propose a non-intrusive cross-model watermarking method that generates discriminative samples as universal keys, enabling authentication without altering model parameters or architectures. Specifically, we introduce a margin enhancement loss to amplify confidence gaps between benign and malicious behaviors, ensuring high transferability across models. Both theoretical analysis and experimental results demonstrate the high efficacy of our proposed method. The generated samples maintain high visual fidelity (SSIM > 0.99), achieve over 3 times higher discriminability than existing methods, retain over 93% accuracy under benign modifications, and detect malicious attacks with accuracy dropping below 9%. Overall, our proposed method provides a robust, transferable, and non-intrusive solution for cross-model authentication, making it ideal for real-world applications where security is critical.

Representation learning technique is an effective link prediction paradigm to alleviate the incompleteness of knowledge hypergraphs. However, the n-ary complex semantic information inherent in knowledge hypergraphs causes existing methods to face the dual limitations of weak effectiveness and low efficiency. In this paper, we propose a novel knowledge hypergraph representation learning model, HySAE, which can achieve a satisfactory trade-off between effectiveness and efficiency. Concretely, HySAE builds an efficient semantic-enhanced 3D scalable end-to-end embedding architecture to sufficiently capture knowledge hypergraph n-ary complex semantic information with fewer parameters, which can significantly reduce the computational cost of the model. In particular, we also design an efficient position-aware entity role semantic embedding way and two enhanced semantic learning strategies to further improve the effectiveness and scalability of our proposed method. Extensive experimental results on all datasets demonstrate that HySAE consistently outperforms state-of-the-art baselines, with an average improvement of 9.15%, a maximum improvement of 39.44%, an average 10.39x faster, and 75.79% fewer parameters.

The Internet of Vehicles (IoV) significantly enhances the capabilities for road information collection and processing by enabling real-time connectivity between vehicles, infrastructure, and cloud systems. Leveraging these technological advantages, multi-vehicle collaborative real-time crack detection is expected to become a crucial method to guarantee the health and safety of infrastructures. Due to different vehicles being equipped with various types of sensors, the collected data are heterogeneous, and the limited computational resources of onboard units obstacle the efficient data processing and effective crack detection in infrastructures. To address these challenges, this study proposes a novel Distributed Edge Computing for Crack detection (DECCD), vehicle serve as edge nodes that locally collect and analyze data. The central node continuously aggregates and processes data from multiple edge nodes to train a robust model. This model is periodically refined and then distributed to edge nodes, where it is further training to detect cracks. A multi-scene dataset, called CrackMS, is constructed by integrating multi-scene datasets of different modalities, and the data are enhanced by Deep Convolutional Generative Adversarial Network (DCGAN) to simulate the complexity of crack data acquired by vehicles. A crack detection model, called the Multi-Scene Auxiliary Prediction Network (MSA-Net), which includes an AUX module and a Scene module is proposed to optimize feature extraction and processing of scene changes. Then a lightweight student model with similar performance is trained by knowledge distillation. Experimental results show that the proposed model, while maintaining a lightweight design, achieves a significant improvement in detection accuracy compared to baseline models.

The widespread availability of similarity queries over trajectory data has led to numerous real-world applications, such as traffic management and path planning. With the proliferation of trajectory data, data owners often outsource storage and computation tasks to the cloud due to limited computing and storage resources. However, this scenario raises sharp security concerns, where it is critical to ensure both the integrity of query results and privacy during query processing. Furthermore, most existing works assume a single-user setting where all query users share the same key, which may lead to query privacy leakage. Therefore, in this article, we take the first step in studying the issue of multiuser and secure trajectory similarity range query (MSRQ). Specifically, inspired by the M-tree, we propose a secure index based on a distributed two-trapdoor public-key cryptosystem (DT-PKC), called M*-tree, and devise secure protocols to support multiuser query processing. We also carefully design a filtering strategy and verification scheme to ensure fast search and integrity guarantees. Finally, we theoretically analyze the security and complexity and empirically evaluate the performance and feasibility of our proposed approach.

Artificial Intelligence Generated Content (AIGC) has rapidly emerged with the capability to generate different forms of content, including text, images, videos, and other modalities, which can achieve a quality similar to content created by humans. As a result, AIGC is now widely applied across various domains such as digital marketing, education, and public health, and has shown promising results by enhancing content creation efficiency and improving information delivery. However, there are few studies that explore the latest progress and emerging challenges of AIGC across different domains. To bridge this gap, this paper brings together 16 scholars from multiple disciplines to provide a cross-domain perspective on the trends and challenges of AIGC. Specifically, the contributions of this paper are threefold: (1) It first provides a broader overview of AIGC, spanning the training techniques of Generative AI, detection methods, and both the spread and use of AI-generated content across digital platforms. (2) It then introduces the societal impacts of AIGC across diverse domains, along with a review of existing methods employed in these contexts. (3) Finally, it discusses the key technical challenges and presents research propositions to guide future work. Through these contributions, this vision paper seeks to offer readers a cross-domain perspective on AIGC, providing insights into its current research trends, ongoing challenges, and future directions.

Knowledge graphs often suffer from incompleteness issues, which can be alleviated through information completion. However, current state-of-the-art deep knowledge convolutional embedding models rely on external convolution kernels and conventional convolution processes, which limits the feature interaction capability of the model. This paper introduces a novel dynamic convolutional embedding model, named ConvD, which directly reshapes relation embeddings into multiple internal convolution kernels. This approach effectively enhances the feature interactions between relation embeddings and entity embeddings. Simultaneously, we incorporate a priori knowledgeoptimized attention mechanism that assigns distinct contribution weights to multiple relational convolution kernels during dynamic convolution, further boosting the expressive power of the model. Extensive experiments on various datasets show that our proposed model consistently outperforms the state-of-the-art baseline methods, with average improvements ranging from 3.28% to 14.69% across all the evaluation metrics, while the number of parameters is reduced by 50.66% to 85.40% compared to other state-of-the-art models.

Session-based Recommender Systems (SBRSs) aim at timely predicting the next likely item by capturing users’ current preferences in sessions. Existing SBRSs research only focuses on maximizing session utilities, and little has been done on the fairness issue in SBRSs, which is vital but different from the same issue in traditional Recommender Systems (RSs). To fill in this gap, we define a novel concept ofsession-oriented fairnessto enforce individual items to have the same exposure accumulated within each single session, which is flexible enough to provide opportunities to achieve different fairness goals. Then, we devise a Session-Oriented Fairness-Aware algorithm (SOFA) with a dual Temporal Convolutional Networks (TCN) architecture: one is SOUP (Session-Oriented Utility Promoter) and the other is SODA (Session-Oriented Disparity Alleviator). Benefit from the collaborative learning of SOUP and SODA for the evolution of accumulated exposure in sessions,SOFAis effective to maximize session-oriented fairness while maintaining high session utilities. To the best of our knowledge, this research is the first to solve fairness issues in SBRSs. Extensive experiments on real-world datasets demonstrate thatSOFAoutperforms the state-of-the-art approaches in terms of both utility and fairness.

Knowledge hypergraph embedding models are usually computationally expensive due to the inherent complex semantic information. However, existing works mainly focus on improving the effectiveness of knowledge hypergraph embedding, making the model architecture more complex and redundant. It is desirable and challenging for knowledge hypergraph embedding to reach a trade-off between model effectiveness and efficiency. In this paper, we propose an end-to-end efficient knowledge hypergraph embedding model, HyCubE, which designs a novel3D circular convolutional neural networkand thealternate mask stackstrategy to enhance the interaction and extraction of feature information comprehensively. Furthermore, our proposed model achieves a better trade-off between effectiveness and efficiency by adaptively adjusting the 3D circular convolutional layer structure to handle$n$-ary knowledge tuples of different arities with fewer parameters. In addition, we use a knowledge hypergraph 1-N multilinear scoring way to accelerate the model training efficiency further. Finally, extensive experimental results on all datasets demonstrate that our proposed model consistently outperforms state-of-the-art baselines, with an average improvement of 8.22% and a maximum improvement of 33.82% across all metrics. Meanwhile, HyCubE is 6.12x faster, GPU memory usage is 52.67% lower, and the number of parameters is reduced by 85.21% compared with the average metric of the latest state-of-the-art baselines.

Group recommendation aims to recommend desired items for a group of users. Existing methods mainly adopt deterministic networks to represent groups as fixed-point vectors, assuming their preferences be highly close to these vectors in interest space. However, each group tends to have various interests, which cannot be fully captured by fixed-point vectors and thus calls for probabilistic modeling of interests as density instead. Although this can be supported by Variational AutoEncoder (VAE), interaction data in group recommendation are highly sparse and insufficient for VAE model training, resulting in high risks of posterior collapse and deficiency in personalization. To this end, this paper proposes a contrastive variational learning model boosted by variational model augmentation and an easyto-hard paradigm. Specifically, VAE with tailored attention is first employed to represent group preferences as variational vectors for probabilistic preference modeling. Additionally, we conduct data-agnostic augmentation via learnable variational dropout, which removes redundant or irrelevant neurons in VAE to generate meaningful augmented views adequately for contrastive learning in spite of data sparsity. Difficulty-aware negative sampling is further applied to generate high-quality negative samples adapting to varying requirements of task difficulty according to the training process. Finally, we utilize density-based variational alignment to guide the optimization process of contrastive learning. Experiments on four real-world datasets are conducted to demonstrate the significant performance improvements of our model compared with SOTA methods for group recommendation.

Bridging the gap between visual and textual modalities effectively has consistently been a key challenge in cross-modal retrieval. Fine-grained matching approaches improve performance by precisely aligning salient region features in visual modality with word embeddings in textual modality. However, how to effectively and efficiently filter out irrelevant features (e.g., irrelevant background regions and nonmeaningful prepositions) in multimodality remains a significant challenge. Furthermore, capturing key cross-modal relationships while minimizing misalignment interference is crucial for effective cross-modal retrieval. In this work, we propose a novel approach called the selective filter and alignment network (SFAN) to tackle these challenges. First, we propose modality-specific selective filter modules (SFMs) to selectively and implicitly filter out redundant information within each modality. We then propose the state-space models (SSMs)-based selective alignment module (SAM) to selectively capture key correspondences and reduce the disturbance of irrelevant associations. Finally, we utilize a fusion operation to combine these embeddings from both SFM and SAM to derive the final embeddings for similarity computation. Extensive experiments on the Flickr30k, MS-COCO, and MSR-VTT datasets reveal that our proposed SFAN can effectively learn robust patterns, significantly outperforming the state-of-the-art (SOTA) cross-modal retrieval methods by a wide margin.

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.

Blockchain has emerged as a prevailing paradigm for decentralized applications due to its reliability and transparency. To scale up retrieval services, a common strategy is to use a hybrid storage model, where on-chain storage is responsible for small metadata and off-chain storage is for outsourced raw data. However, data security and result authenticity are ongoing challenges in this scenario, and little work has been done due to the difficulty of combining result verification and privacy preservation, especially for dynamic updates while supporting forward privacy. In this paper, we formally define the problem of efficient, verifiable, and secure conjunctive keyword search in hybrid-storage blockchains (vsChain) and propose a novel hybrid index that achieves efficient query and verification while supporting dynamic updates with forward privacy guarantee. Finally, we provide empirical evaluations using real and synthetic datasets to demonstrate the feasibility of our proposed scheme.

Group recommendation aims to recommend desired items for a group of users. Due to the sparsity of group-item interactions, existing methods mainly model group preferences by aggregating member-level preference. However, they not only ignore possible user interest drift in specific groups, but also adopt deterministic models to represent group preferences using fixed-points, which are weak in characterizing uncertain group preferences. To this end, following the paradigm of variational learning, this paper proposes a multi-view attentive variational preference aggregation network called GroupAV for group rec-ommendation, so as to conduct user/group preference modeling and aggregation in a density-based manner. Specifically, we first adopt Variational AutoEncoder (VAE) to capture member-level preferences by variational vectors as density. To address user interest drift in groups, a variational preference adapter module is designed to learn group-contextualized preferences via rational transformation in variational space. Next, attentive variational aggregation networks are carefully designed for group-level preference aggregation in two different views (i.e., group-interactions and member-consensus views). Besides, we apply contrastive learning and gating fusion to optimize the multi-view learning process for the final group preference modeling of Mixture-of-Gaussian distribution. Finally, we conduct experiments on real-world datasets and demonstrate GroupAV's significant performance improvements compared to state-of-the-art group recommendation methods.

Probabilistic knowledge graphs (PKGs) improve traditional knowledge graphs by characterizing the inherent uncertainty of knowledge. In PKGs, each triple$(e_{1}, r,e_{2})$, representing that entity$e_{1}$has a relationship$r$with entity$e_{2}$, is associated with a probability$\phi$indicating its likelihood of being true. PKGs have gained popularity in various domains, such as ProBase [22] and NELL [13].

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a valuable supplement to conventional MEC, offering unique advantages in temporary or emergency scenarios. However, the integration of UAVs into MEC introduces new challenges. First, the existing UAV-empowered MEC frameworks predominantly have not accounted for dependencies between the computing tasks. Second, the limited coverage area of UAVs and the sparse distribution of end devices result in a scarcity of training samples, hindering the effectiveness of data-driven approaches. To address these challenges, we introduce a novel UAV-assisted task offloading scheme designed to minimize the average task execution delay and energy consumption in vehicular networks. Initially, we craft a dependency-aware UAV-assisted MEC framework, where the task dependency and priority models are meticulously developed to illustrate the associations between the tasks. Subsequently, we devise an asynchronous federated deep reinforcement learning-based task offloading algorithm, incorporating an asynchronous federated optimization mechanism to enhance the data diversity, with a premise of ensuring data privacy. Our method has been rigorously tested using real traffic flow data and a directed acyclic graph task data set. Comparative experiments highlight the superiority of our framework, showcasing a substantial reduction in the average task delay and energy consumption.

Recent advancements in TableQA leverage sequence-to-sequence (Seq2seq) deep learning models to accurately respond to natural language queries. These models achieve this by converting the queries into SQL queries, using information drawn from one or more tables. However, Seq2seq models often produce uncertain (low-confidence) predictions when distributing probability mass across multiple outputs during a decoding step, frequently yielding translation errors. To tackle this problem, we present Ckif , a confidence-based knowledge integration framework that uses a two-stage deep-learning-based ranking technique to mitigate the low-confidence problem commonly associated with Seq2seq models for TableQA. The core idea of Ckif is to introduce a flexible framework that seamlessly integrates with any existing Seq2seq translation models to enhance their performance. Specifically, by inspecting the probability values in each decoding step, Ckif first masks out each low-confidence prediction from the predicted outcome of an underlying Seq2seq model. Subsequently, Ckif integrates prior knowledge of query language to generalize masked-out queries, enabling the generation of all possible queries and their corresponding NL expressions. Finally, a two-stage deep-learning ranking approach is developed to evaluate the semantic similarity of NL expressions to a given NL question, hence determining the best-matching result. Extensive experiments are conducted to investigate Ckif by applying it to five state-of-the-art Seq2seq models using a widely used public benchmark. The experimental results indicate that Ckif consistently enhances the performance of all the Seq2seq models, demonstrating its effectiveness for better supporting TableQA.

The next point-of-interest (POI) recommendation aims to predict users’ future movements based on their historical trajectories. However, in reality, users may provide uncertain check-in records, resulting in uploaded data that lack precise location information and is instead ambiguous. Despite this challenge, only a limited number of studies have addressed this issue, often overlooking the intricate interactions among users, POIs, and POI categories. To that end, we propose a novel model called knowledge-graph-enhanced transformer (KGNext). KGNext leverages transition and interaction graphs derived from our constructed transitional-interactive knowledge graph (TIKG) to uncover both general movement patterns and varied user preferences regarding POIs and POI categories. Furthermore, KGNext integrates comprehensive contextual information from historical trajectories with TIKG to generate user trajectory embeddings. These encoded features are then utilized by a transformer model to provide fine-grained predictions of the next POI. Experimental results on three real-world datasets demonstrate the superiority of KGNext.

Blockchain has emerged as a prevailing paradigm for decentralized applications due to its reliability and transparency. To scale up retrieval services, a common strategy is to use a hybrid storage model, where on-chain storage is responsible for small metadata and off-chain storage is for outsourced raw data. However, data security and result authenticity are ongoing challenges in this scenario, and little work has been done due to the difficulty of combining result verification and privacy preservation, especially for dynamic updates while supporting forward privacy. In this paper, we formally define the problem of efficient, verifiable, and secure conjunctive keyword search in hybrid-storage blockchains (vsChain) and propose a novel hybrid index that achieves efficient query and verification while supporting dynamic updates with forward privacy guarantee. We also design two optimized schemes to improve query and verification performance by using a partition-based method and an obfuscated counting Bloom filter mechanism. Finally, we provide a theoretical security analysis and empirical evaluations using real and synthetic datasets to demonstrate the feasibility of our proposed schemes.

Data outsourcing to the cloud has become increasingly popular for high-speed storage and retrieval. However, privacy and security are pressing concerns that hinder the further development of cloud computing. A common approach is to encrypt data before outsourcing, assuming the cloud is semi-honest. However, in reality, the cloud may be malicious and forge query results unexpectedly. Moreover, most previous schemes are designed for single-user setting, where different users share the same secret key, leading to potential privacy leaks. Therefore, ensuring confidentiality and verifiability in multi-user setting is crucial but has not been well-addressed. In this paper, we formally define the notion of Verifiable and Secure Range Query in Multi-User Setting (VSRQM) and propose a prefix-aware encoding (Pcode) scheme to encode spatial data for query processing. Next, we design a Tree-Aided Verifiable and Secure Index (SATree) on top of the Pcode and symmetric re-encryption scheme. SATree preserves data privacy, provides a mechanism to verify query results’ integrity and achieves sub-linear search time. Additionally, we propose two compression schemes to reduce the space cost of storage and transmission. Finally, we present formal complexity and security analyses and conduct empirical evaluations on real and synthetic datasets to demonstrate our proposed approaches’ practical performance.

Searching for well-connected user communities in a Location-based Social Network (LBSN) has been extensively investigated. However, very few studies focus on finding a group of locations in an LBSN which are significantly engaged with socially cohesive user groups. In this work, we investigate the problem ofCo-engagedLocation groupSearch (CLS) from LBSNs where the selected locations are visited frequently by the members of the socially cohesive user groups, and the locations are reachable within a given distance threshold. To the best of our knowledge, this is the first work to search for socially co-engaged location groups in LBSNs. We devise a score function to measure the co-engagement of the location groups by combining social connectivity of the cohesive user groups and check-in density of the users to the selected locations. To solve theCLSproblem, we propose aFilter-and-Verifyalgorithm that effectively filters out ineligible locations, and their corresponding check-in users. Further, we derive a lower bound on the number of check-ins to prune the insignificant locations and develop a novel greedy forward expansion algorithm (GFA). To accelerate the computation ofCLS, we propose a ranking function and devise an incremental algorithm,GIA, that can filter the unqualified location groups. We establish the effectiveness of our solutions by conducting extensive experiments on three real-world datasets.

Knowledge hypergraph representation learning, which projects entities and$n$-ary relations into a low-dimensional vector space, remains a challenging area to be explored despite the ubiquity of$n$-ary relational facts in the real world. Current methods are always extensions of those used for knowledge graphs with shallow or deep structures. However, shallow and linear models limit the extraction capacity of the latent knowledge, while deep and non-linear models lead to the overabundance of parameters. In this paper, we propose a novel knowledge hypergraph completion model called HJE, which utilizes the powerful capability of convolutional neural networks for efficient representation learning. Interaction-enhanced 3D convolution and relation-aware 2D convolution are jointly utilized by HJE to extract explicit and implicit global knowledge and semantic information effectively without compromising the translation property of the model. Moreover, HJE constructs a unified learnable embedding matrix to capture entity position information in knowledge tuples. The entity mask mechanism can naturally couple the multilinear scoring approach for$n$-ary facts to speed up the training convergence of the model. Extensive experimental results on real datasets of knowledge hypergraphs and knowledge graphs demonstrate the superior performance of HJE compared with state-of-the-art baselines.

Community search over large dynamic graph has become an important research problem in modern complex networks, such as the online social network, collaboration network and biological networks. Network data in the time-varied environment has motivated several recent studies to identify the evolution of the communities. However, these studies mostly match communities of different snapshot or utilize the aggregation of the disjoint structural information and ignores the cohesion continuity. To fill this research gap, in this work, we propose a novel$(\theta ,k)$-core reliable community (CRC) and define the reliable community search problem which jointly considers member engagement, connection strength and cohesion continuity of the community in the dynamic network. We propose an online search algorithm based on eligible edge filtering and we further construct the Weighted Core Forest-Index (WCF-index) and develop efficient index-based querying algorithm with strong pruning properties. We also propose top-$l$reliable community search problem that couples query based distance to reduce the free rider effect in local community search and support flexible multiple query vertices. Extensive experiments are conducted to show the efficiency and effectiveness of the proposed algorithms.

The 5G networks have extensively promoted the growth of mobile users and novel applications, and with the skyrocketing user requests for a large amount of popular content, the consequent content delivery services (CDSs) have been bringing a heavy load to mobile service providers. As a key mission in intelligent networks management, understanding and predicting the distribution of CDSs benefits many tasks of modern network services such as resource provisioning and proactive content caching for content delivery networks. However, the revolutions in novel ubiquitous network architectures led by ultra-dense networks (UDNs) make the task extremely challenging. Specifically, conventional methods face the challenges of insufficient spatio precision, lacking generalizability, and complex multi-feature dependencies of user requests, making their effectiveness unreliable in CDSs prediction under 5G UDNs. In this article, we propose to adopt a series of encoding and sampling methods to model CDSs of known and unknown areas at a tailored fine-grained level. Moreover, we design a spatio-temporal-social multi-feature extraction framework for CDSs hotspots prediction, in which a novel edge-enhanced graph convolution block is proposed to encode dynamic CDSs networks based on the social relationships and the spatio features. Besides, we introduce the Long-Short Term Memory (LSTM) to further capture the temporal dependency. Extensive performance evaluations with real-world measurement data collected in two mobile content applications demonstrate the effectiveness of our proposed solution, which can improve the prediction area under the curve (AUC) by 40.5% compared to the state-of-the-art proposals at a spatio granularity of 76m, with up to 80% of the unknown areas.

In recent years, distributed graph convolutional networks (GCNs) training frameworks have achieved great success in learning the representation of graph-structured data with large sizes. However, existing distributed GCN training frameworks require enormous communication costs since a multitude of dependent graph data need to be transmitted from other processors. To address this issue, we propose a graph augmentation-based distributed GCN framework (GAD). In particular, GAD has two main components: GAD-Partition and GAD-Optimizer. We first propose an augmentation-based graph partition (GAD-Partition) that can divide the input graph into augmented subgraphs to reduce communication by selecting and storing as few significant vertices of other processors as possible. To further speed up distributed GCN training and improve the quality of the training result, we design a subgraph variance-based importance calculation formula and propose a novel weighted global consensus method, collectively referred to as GAD-Optimizer. This optimizer adaptively adjusts the importance of subgraphs to reduce the effect of extra variance introduced by GAD-Partition on distributed GCN training. Extensive experiments on four large-scale real-world datasets demonstrate that our framework significantly reduces the communication overhead ( ≈ 50% ), improves the convergence speed ( ≈ 2 × ) of distributed GCN training, and obtains a slight gain in accuracy ( ≈ 0.45% ) based on minimal redundancy compared to the state-of-the-art methods.

Federated Learning (FL), as a distributed machine learning technique, has promise for training models with distributed data in Artificial Intelligence of Things (AIoT). However, FL is vulnerable to Byzantine attacks from diverse participants. While numerous Byzantine-robust FL solutions have been proposed, most of them involve deploying defenses at either the aggregation server or the participant level, significantly impacting the original FL process. Moreover, it will bring extra computational burden to the server or the participant, which is especially unsuitable for the resource-constrained AIoT domain. To resolve the aforementioned concerns, we propose FL-Auditor, a Byzantine-robust FL approach based on public auditing. Its core idea is to use a Third-Party Auditor (TPA) to audit samples from the FL training process, analyzing the trustworthiness of different participants, thereby helping FL obtain a more robust global model. In addition, we also design a lazy update mechanism to reduce the negative impact of sampling audit on the performance of the global model. Extensive experiments have demonstrated the effectiveness of our FL-Auditor in terms of accuracy, robustness against attacks, and flexibility. In particular, compared to the existing method, our FL-Auditor significantly reduces the computation time on the aggregation server by 8×-17×.

Temporal Knowledge Graphs (TKGs), which record the evolution of relationships among entities over time, have been increasingly used in a myriad of applications. Despite their growing importance, the challenge of aligning entities in these evolving structures has yet to be satisfactorily addressed. Most existing techniques struggle to keep pace with the continual stream of new entities and relations, which is a defining characteristic of TKGs. In response to this challenge, we propose a novel teacher-student approach for incremental entity alignment in evolving TKGs. Our solution leverages a Graph Attention Network (GAT) as the teacher model and a sampling Graph Convolutional Network (GCN) as a lightweight, adaptable student model. This approach efficiently navigates the evolving complexities inherent in TKGs, leading to remarkable improvements in the efficiency and effectiveness of entity alignment. The experimental results substantiate the superior performance of our approach in achieving effective entity alignment promptly, outstripping existing state-of-the-art models. As such, our study contributes a crucial step towards efficiently handling evolving entity alignment tasks in TKGs.

Knowledge graph completion (KGC) predicts missing links and is crucial for real-life knowledge graphs, which widely suffer from incompleteness. KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time. Emerging methods have recently shown improved prediction results by further incorporating the temporal validity of facts; namely, temporal knowledge graph completion (TKGC). With this temporal information, TKGC methods explicitly learn the dynamic evolution of the knowledge graph that KGC methods fail to capture. In this paper, for the first time, we comprehensively summarize the recent advances in TKGC research. First, we detail the background of TKGC, including the preliminary knowledge, benchmark datasets, and evaluation metrics. Then, we summarize existing TKGC methods based on how the temporal validity of facts is used to capture the temporal dynamics. Finally, we conclude the paper and present future research directions of TKGC.

Most existing multi-view learning methods assume that the dimensions of different views are similar. In real-world applications, it is often the case that the dimension of a view may be extremely small compared with these of other views, resulting in an unbalanced multi-view learning problem. Previous methods for this problem have at least one of the following drawbacks: (1) despising the information of low dimensional views; (2) constructing balanced view-specific inter-instance similarity graphs or employing decision-level fusion, which cannot well learn multi-level inter-view correlations and is limited to category-related tasks such as clustering. To eliminate all these drawbacks, we present an Unbalanced Multi-view Deep Learning (UMDL) method. Considering a low dimensional view usually contains multiple patterns, we construct an overcomplete dictionary with its atoms exceeding the dimension of the original data. We transfer the original data into a combination of atoms and obtain a higher dimensional representation. We propose a sparse multi-view fusion paradigm to explicitly capture the complementarity of multi-view data in a flexible manner. Moreover, we construct positive and negative examples via balanced similarity graphs and employ contrastive learning to train UMDL in a self-supervised manner. Experiments conducted on a toy example and 7 balanced/unbalanced datasets show that UMDL outperforms baseline methods and can be well applied to downstream classification and segmentation tasks. The code is released at https://github.com/xdmvteam/UMDL.

Knowledge hypergraph embedding, which projects entities and n-ary relations into a low-dimensional continuous vector space to predict missing links, remains a challenging area to be explored despite the ubiquity of n-ary relational facts in the real world. Currently, knowledge hypergraph link prediction methods are essentially simple extensions of those used in knowledge graphs, where n-ary relational facts are decomposed into different subelements. Convolutional neural networks have been shown to have remarkable information extraction capabilities in previous work on knowledge graph link prediction. In this paper, we propose a novel embedding-based knowledge hypergraph link prediction model named HyConvE, which exploits the powerful learning ability of convolutional neural networks for effective link prediction. Specifically, we employ 3D convolution to capture the deep interactions of entities and relations to efficiently extract explicit and implicit knowledge in each n-ary relational fact without compromising its translation property. In addition, appropriate relation and position-aware filters are utilized sequentially to perform two-dimensional convolution operations to capture the intrinsic patterns and position information in each n-ary relation, respectively. Extensive experimental results on real datasets of knowledge hypergraphs and knowledge graphs demonstrate the superior performance of HyConvE compared with state-of-the-art baselines.

Cohesive multipartite subgraphs (CMS) in heterogeneous information networks (HINs) uncover closely connected vertex groups of multiple types, enhancing real applications like community search and anomaly detection. However, existing works for HINs pay less attention to searching CMS. In this paper, we leverage well-established concepts of meta-path and densest subgraph to propose a novel CMS model called the densest P -partite subgraph. Given a multipartite subgraph of an HIN induced by i =| P | types of vertices defined in a query meta-path P (i.e., a P -partite subgraph), we devise a novel density function which is the number of the instances of P over the geometric mean of the sizes of i different types of vertex sets in the subgraph. A P -partite subgraph with the highest density serves as the optimum result. To find the densest P -partite subgraph in an HIN with n vertices, we first design an exact algorithm with a runtime cost equivalent to solving Θ(|M|) instances of the min-cut problem where |M|= O (( n/i ) i ). Then, we attempt a more efficient approximation algorithm that achieves a ratio of 1/ i but still incurs the cost of solving Θ(|M|) instances of our proposed peeling problem. Both approaches struggle with scalability due to Θ(|M|). To overcome this bottleneck, we improve the exact algorithm with novel pruning rules that non-trivially reduce the number of min-cut problem instances to solve to O (|M|). Empirically, 70-90% instances are pruned, making the improved exact algorithm significantly faster than the approximation algorithm. Extensive experiments on real datasets demonstrate the effectiveness of the proposed model and the efficiency of our algorithms.

Nowadays, many edge computing service providers expect to leverage the computational power and data of edge nodes to improve their models without transmitting data. Federated learning facilitates collaborative training of global models among distributed edge nodes without sharing their training data. Unfortunately, existing privacy-preserving federated learning applied to this scenario still faces three challenges: 1) It typically employs complex cryptographic algorithms, which results in excessive training overhead; 2) It cannot guarantee Byzantine robustness while preserving data privacy; and 3) Edge nodes have limited computing power and may drop out frequently. As a result, the privacy-preserving federated learning cannot be effectively applied to edge computing scenarios. Therefore, we propose a lightweight and secure federated learning scheme LSFL, which combines the features of privacy-preserving and Byzantine-Robustness. Specifically, we design the Lightweight Two-Server Secure Aggregation protocol, which utilizes two servers to enable secure Byzantine robustness and model aggregation. This scheme protects data privacy and prevents Byzantine nodes from influencing model aggregation. We implement and evaluate LSFL in a LAN environment, and the experiment results show that LSFL meets fidelity, security, and efficiency design goals, and maintains model accuracy compared to the popular FedAvg scheme.

As an essential approach in many Internet of Things (IoT) applications, multiview learning synthesizes multiple features to achieve more comprehensive descriptions of data items. Most of the previous studies on multiview learning have been dedicated to increasing the prediction accuracy, while ignoring the reliability of the decision. This would limit their deployment in high-risk IoT and industrial applications such as the automated vehicle. Although a trusted multiview classification model has been proposed recently, it cannot well deal with the highly complementary multiview data. In this work, we present an evidential multiview deep learning (EMDL) method to make reliable decisions. EMDL first seeks view-specific evidence of each category, which could be termed as the amount of support to each category collected from data. It then dynamically fuses different views at the evidence level to construct the multiview common evidence and makes reliable prediction accordingly (strong evidence indicates high prediction confidence). In particular, we establish a degradation layer to learn the mappings from the common evidence (comprehensive information) to view-specific evidences (partial information) for evidence fusion. It aims to explicitly model consistent and complementary relations in multiview data at the evidence level. We apply EMDL on a synthetic toy dataset and five real-world datasets (three datasets are related to industrial scenarios). Experiments show that EMDL outperforms state-of-the-art baseline methods.

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.

With the boom in cloud computing, data outsourcing in location-based services is proliferating and has attracted increasing interest from research communities and commercial applications. Nevertheless, since the cloud server is probably both untrusted and malicious, concerns about data security and result integrity have become on the rise sharply. In addition, in the single-user situation assumed by most existing works, query users can capture query content from each other even though the queries are encrypted, which may incur the leakage of query privacy. Unfortunately, there exists little work that can commendably assure data security and result integrity in the multi-user setting. To this end, in this article, we study the problem of multi-user, secure, and verifiable$k$nearest neighbor query (MSV$k$kNN). To support MSV$k$NN, we first propose a novel unified structure, called verifiable and secure index (VSI). Based on this, we devise a series of secure protocols to facilitate query processing and develop a compact verification strategy. Given an MSV$k$NN query, our proposed solution can not merely answer the query efficiently while can guarantee: 1) preservingdata privacy,query privacy,result privacy, andaccess patterns privacy; 2) authenticating thecorrectnessandcompletenessof the results; 3) supportingmulti-userwith different keys. Finally, the formal security analysis and complexity analysis are theoretically proven and the performance and feasibility of our proposed approach are empirically evaluated and demonstrated.

The development of information gathering and extraction technology has led to the popularity of multi-view data, which enables samples to be seen from numerous perspectives. Multi-view clustering (MVC), which groups data samples by leveraging complementary and consensual information from several views, is gaining popularity. Despite the rapid evolution of MVC approaches, there has yet to be a study that provides a full MVC roadmap for both stimulating technical improvements and orienting research newbies to MVC. In this article, we review recent MVC techniques with the purpose of exhibiting the concepts of popular methodologies and their advancements. This survey not only serves as a unique MVC comprehensive knowledge for researchers but also has the potential to spark new ideas in MVC research. We summarise a large variety of current MVC approaches based on two technical mechanisms: heuristic-based multi-view clustering (HMVC) and neural network-based multi-view clustering (NNMVC). We end with four technological approaches within the category of HMVC: nonnegative matrix factorisation, graph learning, latent representation learning, and tensor learning. Deep representation learning and deep graph learning are two technical methods that we demonstrate in NNMVC. We also show 15 publicly available multi-view datasets and examine how representative MVC approaches perform on them. In addition, this study identifies the potential research directions that may require further investigation in order to enhance the further development of MVC.

With the advent of location-based social networks, users can tag their daily activities in different locations through check-ins. These check-in locations signify user preferences for various socio-spatial activities and can be used to improve the quality of services in some applications such as recommendation systems, advertising, and group formation. To support such applications, in this paper, we formulate a new problem of identifying top-k Socio-Spatial co-engaged Location Selection (SSLS) for users in a social graph, that selects the best set of k locations from a large number of location candidates relating to the user and her friends. The selected locations should be (i) spatially and socially relevant to the user and her friends, and (ii) diversified both spatially and socially to maximize the coverage of friends in the socio-spatial space. To address the NP-hard and challenging problem, we first develop an exact solution by designing some pruning strategies, and also develop an approximate solution by deriving relaxed bounds and advanced termination rules. To accelerate the efficiency, we further develop a fast exact approach and a meta-heuristic approximate approach. Finally, extensive experiments are conducted to evaluate the performance of our proposed algorithms against three adapted existing methods using four real-world datasets.

Fastest route recommendation (FRR) is crucial for intelligent transportation systems. The existing methods treat it as a pathfinding problem on dynamic graphs, and extend A* algorithm with neuralized travel time estimators as cost functions. However, they fail to provide effective heuristic cost due to the neglect of its admissibility and the utilization of noise path information, resulting in sub-optimal results and inefficiency. Besides, path sequentiality is also ignored, affecting algorithm accuracy as well. In this paper, we propose a variational inference based fastest route recommendation method, which follows the framework of A* algorithm and provides effective costs for routing. Specifically, we first adopt a sequential estimator to accurately estimate the travel time of a specific path. More importantly, we design a variational inference based estimator, which models the distribution of travel time between two nodes and provides an effective heuristic cost with high probability of being admissible. We further take advantage of adversarial learning to enrich the fastest path information. To the best of our knowledge, we are the first to use variational estimator to consider the admissibility of heuristics in FRR. Extensive experiments are conducted on two real-world datasets. The results verify the performance advantage of our proposed method.

Graph Convolutional Network (GCN) has achieved extraordinary success in learning representations of nodes in graphs. However, regarding Heterogeneous Information Network (HIN), existing HIN-oriented GCN methods still suffer from two deficiencies: (1) they cannot flexibly explore all possible meta-paths and extract the most useful ones for each target object, which hinders both effectiveness and interpretability; (2) before performing aggregation, they often require some additional time-consuming pre-processing operations, which increase the computational complexity. To address the above issues, we propose an interpretable and efficient Heterogeneous Graph Convolutional Network (ie-HGCN) to learn the representations of objects in HINs. It is designed as a hierarchical aggregation architecture, i.e., object-level aggregation and type-level aggregation. The new architecture can automatically evaluate all possible meta-paths within a length limit, and discover and exploit the most useful ones for each target object, i.e., at fine granularity. It also reduces the computational cost by avoiding additional time-consuming pre-processing operations. Theoretical analysis shows its ability to evaluate the usefulness of all possible meta-paths, its connection to the spectral graph convolution on HINs, and its quasi-linear time complexity. Extensive experiments on four real network datasets demonstrate its interpretability, efficiency as well as its superiority against thirteen baselines.

In the 5 G era, data traffic has been growing rapidly. A small number of popular data files may dominate the network traffic and lead to heavy network congestion. Device-to-Device (D2D) communication can be used for caching and offloading significant data traffic. D2D social networks are instantiated paradigms of D2D communication. Existing studies maximize the performances of caching and offloading in D2D social networks by predicting potential content propagation paths. However, predicting such paths still faces many challenges, such as limitation of user spatial-temporal features, fragility of D2D social networks, and uncertainty of participants. As a solution, we first measure users' multi-dimensional features and content propagation paths to explore the distributions of D2D activities. Then we propose a D2D-LSTM model to predict complete content propagation paths hierarchically and design a prototype-user model for new participants. Experimental results demonstrate the state-of-the-art performances of D2D-LSTM. D2D-LSTM achieves at most 95% and at least 84.6% average precision in predicting terminal prototype-user class. Tree generation tests show that the generated trees have at most 64% and at least 17% similarity with ground-truth trees.

The combination of mobile edge computing and 5G heterogeneous networks (5G HetNets) provides new vehicular task offloading research solutions. Most existing task offloading studies assume that vehicle tasks are unique and there are no redundant tasks between vehicles. However, there is a duplication of tasks for vehicles within the same base station. That causes a waste of computing resources and increases task offloading costs. To address this problem, this paper proposes the task offloading algorithm TOERT to eliminate redundant tasks in 5G HetNets. The TOERT algorithm is designed to eliminate redundant tasks, improve vehicle task completion rates and reduce offloading costs. Specifically, we consider two cases of redundant tasks within the macro cell base station (MCBS). When the task results have been stored in the MCBS, vehicles directly agree on the transaction price with the MCBS to obtain the task results. The MCBS first eliminates redundant tasks between vehicles when task results are not stored. Then, the MCBS determines the appropriate small cell base station (SCBS) to participate in the partial offloading. Finally, the vehicles negotiate with the MCBS to obtain task results. Against the other five algorithms considered for comparison purposes, the TOERT algorithm effectively eliminates redundant tasks, improves the task completion rate and increases the benefits of both the vehicles and the MCBS.

Abstract Graph learning is being increasingly applied to image clustering to reveal intra-class and inter-class relationships in data. However, existing graph learning-based image clustering focuses on grouping images under a single view, which under-utilises the information provided by the data. To address that, we propose a self-supervised multi-view image clustering technique under contrastive heterogeneous graph learning. Our method computes a heterogeneous affinity graph for multi-view image data. It conducts Local Feature Propagation (LFP) for reasoning over the local neighbourhood of each node and executes an Influence-aware Feature Propagation (IFP) from each node to its influential node for learning the clustering intention. The proposed framework pioneeringly employs two contrastive objectives. The first targets to contrast and fuse multiple views for the overall LFP embedding, and the second maximises the mutual information between LFP and IFP representations. We conduct extensive experiments on the benchmark datasets for the problem, i.e. COIL-20, Caltech7 and CASIA-WebFace. Our evaluation shows that our method outperforms the state-of-the-art methods, including the popular techniques MVGL, MCGC and HeCo.

Abstract Entity alignment is an essential process in knowledge graph (KG) fusion, which aims to link entities representing the same real-world object in different KGs, to achieve entity expansion and graph fusion. Recently, embedding-based entity pair similarity evaluation has become mainstream in entity alignment research. However, these methods heavily rely on labelled entity pairs, which are often unavailable. Some self-supervised methods exploit features of KGs regardless of noise when generating aligned entity pairs. To resolve this issue, we propose a generative adversarial entity alignment method, which is more robust to noise data. The proposed method then exploits both attribute and structure information in the KGs and applies a BERT-based contrastive loss function to embed entities in KGs. Experimental results on several benchmark datasets demonstrate the superiority of our framework compared with most existing state-of-the-art entity alignment methods.

Abstract The partial evaluation and assembly framework has recently been applied for processing subgraph matching queries over large-scale knowledge graphs in the distributed environment. The framework is implemented on the master-slave architecture, endowed with outstanding scalability. However, there are two drawbacks of partial evaluation: if the volume of intermediate results is large, a large number of repeated partial matches will be generated; and the assembly computation handled by the master would be a bottleneck. In this paper, we propose an optimal partial evaluation algorithm and a filter method to reduce partial matches by exploring the computing characteristics of partial evaluation and assembly framework. (1) An index structure named inner boundary node index (IBN-Index) is constructed to prune for graph exploration to improve the searching efficiency of the partial evaluation phase. (2) The boundary characteristics of local partial matches are utilized to construct a boundary node index (BN-Index) to reduce the number of local partial matches. (3) The experimental results over benchmark datasets show that our approach outperforms the state-of-the-art methods.

Link prediction in knowledge hypergraphs has been recognized as a critical issue in various downstream tasks for knowledge-enabled applications, from question answering to recommender systems. However, most existing approaches are primarily performed in a black-box fashion, which learn low-dimensional embeddings for inference, thus cannot provide human-understandable interpretation. In this paper, we present HyperMLN, an n-ary, mixed, and explainable framework that interprets the path-reasoning process with first-order logic, which provides a knowledge-enhanced interpretable prediction framework, in which domain knowledge in the logic rules improves the performance of embedding models, while semantic information in the embedding space can optimize the weight of the logic rules in turn. To provide benchmark rule sets for explainable link prediction methods, three types of meta-logic rules in each popular dataset are mined for interpreting results. While achieving explainability, our framework also realizes an average improvement of 3.2% on [email protected] compared to the state-of-the-art knowledge hypergraph embedding method. Our code is available at https://github.com/zirui-chen/HyperMLN.

With the popularization and application of Artificial Intelligence technology, knowledge graph embedding methods are widely used for a variety of machine learning tasks. However, most of the current knowledge graph embedding models are trained with a large number of parameters and high computational time complexity. This becomes a main obstacle to apply these existing models to large-scale knowledge graphs. To address this challenge, we propose HET-KG, a distributed system for training knowledge graph embedding efficiently. HET-KG can reduce the communication overheads by introducing a cache embedding table structure to maintain hot-embeddings at each worker. To improve the effectiveness of the cache mechanism, we design a prefetching algorithm and a filtering algorithm for adaptively selecting hot-embeddings, and provide two kinds of hot-embedding table construction strategies. To address the issue of inconsistency between the local cached hot-embeddings and the global embeddings, we also develop a hot-embedding synchronization algorithm for dynamically updating the cache embedding table, which can guarantee the inconsistency bounded within a given threshold. Finally, extensive experiments are conducted on three knowledge graph datasets FB15k, WN18, and Freebase-86m. The experimental results show that HET-KG achieves 3.7x and 1.1x speedup over the state-of-the-art systems PyTorch-BigGraph and DGL-KE, respectively.

En route travel time estimation (ER-TTE) aims to predict the travel time on the remaining route. Since the traveled and remaining parts of a trip usually have some common characteristics like driving speed, it is desirable to explore these characteristics for improved performance via effective adaptation. This yet faces the severe problem of data sparsity due to the few sampled points in a traveled partial trajectory. Since trajectories with different contextual information tend to have different characteristics, the existing meta-learning method for ER-TTE cannot fit each trajectory well because it uses the same model for all trajectories. To this end, we propose a novel adaptive meta-learning model called MetaER-TTE. Particularly, we utilize soft-clustering and derive cluster-aware initialized parameters to better transfer the shared knowledge across trajectories with similar contextual information. In addition, we adopt a distribution-aware approach for adaptive learning rate optimization, so as to avoid task-overfitting which will occur when guiding the initial parameters with a fixed learning rate for tasks under imbalanced distribution. Finally, we conduct comprehensive experiments to demonstrate the superiority of MetaER-TTE.

Traffic flow prediction is a challenging task due to complex spatial-temporal correlations. Most existing methods leverage graph convolutional network (GCN) to capture spatial correlations. However, GCN has limited ability in mining global spatial correlations. Multi-layer GCN for aggregating multi-order neighbor information will result in high-degree nodes being prone to over-smoothing. To this end, we develop a graph convolutional recurrent attention network (GCRAN) for traffic flow prediction. Specifically, we take the advantage of Gated Recurrent Units (GRU) and Attention to explore local and global temporal correlations. Moreover, we design a novel local context aware spatial attention to extract local and global spatial correlations simultaneously. Experiments on two public real-world traffic datasets demonstrate that GCRAN outperform state-of-the-art baselines.

Reinforcement learning has been applied to various decision-making tasks and has achieved high profile successes. More and more studies have proposed to use reinforcement learning (RL) for traffic signal control to improve transportation efficiency. However, these methods suffer from a major exploration problem, and their performance is particularly poor. And even fail to quickly converge during the initial stage when interacting with the environment. To overcome this problem, we propose an RL model for traffic signal control based on demonstration data, which provides prior expert knowledge before RL model training. The demonstrations are collected from the classic method self-organizing traffic light (SOTL). It not only serves as expert knowledge but also explores and improves the entire decision-making system. Specifically, we use small demonstration data sets to pre-train the Ape-X Deep Q-learning Network (DQ N) for traffic signal control. When training a RL model from scratch, we often need a lot of data and time to learn a better initialization. Our approach is dedicated to making the RL algorithm converge quickly and accelerating the pace of learning. Extensive experiments on three urban datasets confirm that our method performs better with faster convergence and least travel time than the current RL-based methods by an average of 23.9%, 23.8%, 11.6%

Spatial keyword query has attracted wide-spread academic and industrial concerns due to the popularity of location-based services and Internet of Things. To efficiently support the online query processing, the data owners need to outsource their data to cloud platforms. However, the outsourcing services may raise privacy leaking issues. Moreover, access control, another important security concern, is largely ignored. Therefore, we first propose and formalize the problem of secure boolean spatial keyword query under lightweight access control while guaranteeing the widely acceptedadaptive indistinguishability against chosen keyword attackmodel. Then, we devise a novel hybrid Bloom filter encoding strategy, including a linear embedding and exponent-based transformation schemes and a secure index structure, calledSAGTree. They can maintain both geo-text and access policy information together in a secure way while answering the encrypted queries under access control without decryption. Finally, we present the in-depth security analysis and demonstrate the performance of our proposed algorithms.

With the rapid prosperity of wireless communication, a new era of smart cities has arisen. During the implementation of smart cities, electricity supply plays a profound role and needs to be guaranteed. To do this, a health model with terminal sensors data is usually adopted to enable the security of the transmission line. However, due to the extreme weather condition and the interference of sensors, some transmitted values are missing, which renders the sensors unreliable. Unfortunately, the existing prediction methods have a lower accuracy and there still exists a gap between the real value and the predicted value. In this article, we propose a novel data fusion model, named as KNN-XGBoost, to predict the missing values. Specifically, in this model, it mainly consists of two parts: 1) KNN stage: a KNN model is used to find${K}$nearest labels as new attributes and 2) XGBoost: an XGBoost model is applied to predict missing attribute values. The experimental results have demonstrated that our proposed model can improve the accuracy from 1.11% to 4.05%, 0.55% to 1.84%, and 1.10% to 4.04% than the other methods under the three grid health models, respectively.

Maximal bicliques are effective to reveal meaningful information hidden in bipartite graphs. Maximal biclique enumeration (MBE) is challenging since the number of the maximal bicliques grows exponentially w.r.t. the number of vertices in a bipartite graph in the worst case. However, a large bipartite graph is usually very sparse, which is against the worst case and may lead to fast MBE algorithms. The uncharted opportunity is taking advantage of the sparsity to substantially improve the MBE efficiency for large sparse bipartite graphs. We observe that for a large sparse bipartite graph, a vertex u may converge to a few vertices in the same vertex set as u via its neighbours, which reveals that the enumeration scope for a vertex could be very small. Based on this observation, we propose novel concepts: unilateral coreness for individual vertices, unilateral order for each vertex set and unilateral convergence (ζ) for a large sparse bipartite graph, ζ could be a few thousand for a large sparse bipartite graph with hundreds of million edges. Using the unilateral order, every vertex with τ unilateral coreness only needs to check at most 2 τ combinations so that all maximal bicliques can be enumerated and τ is bounded by ζ, which leads to a novel MBE algorithm running in O * (2 ζ ). We then propose a batch-pivots technique to eliminate all enumerations resulting in non-maximal bicliques, which guarantees that every maximal biclique is reported in O (ζ e )-delay, where e is the number of edges. We devise novel data structures that allow storing subgraphs at omissible space for further speeding up MBE. Extensive experiments are conducted on synthetic and real large datasets to justify that our proposed algorithm is faster and more scalable than the existing algorithms.

Searching for local communities is an important research problem that supports advanced data analysis in various complex networks, such as social networks, collaboration networks, cellular networks, etc. The evolution of such networks over time has motivated several recent studies to identify local communities in dynamic networks. However, these studies only utilize the aggregation of disjoint structural information to measure the quality and ignore the reliability of the communities in a continuous time interval. To fill this research gap, we propose a novel (θ, k )- core reliable community (CRC) model in the weighted dynamic networks, and define the problem of most reliable community search that couples the desirable properties of connection strength, cohesive structure continuity, and the maximal member engagement. To solve this problem, we first develop a novel edge filtering based online CRC search algorithm that can effectively filter out the trivial edge information from the networks while searching for a reliable community. Further, we propose an index structure, Weighted Core Forest-Index (WCF-index), and devise an index-based dynamic programming CRC search algorithm, that can prune a large number of insignificant intermediate results and support efficient query processing. Finally, we conduct extensive experiments systematically to demonstrate the efficiency and effectiveness of our proposed algorithms on eight real datasets under various experimental settings.

Attributed networks are ubiquitous in the real world, such as social networks. Therefore, many researchers take the node attributes into consideration in the network representation learning to improve the downstream task performance. In this article, we mainly focus on an untouched "oversmoothing" problem in the research of the attributed network representation learning. Although the Laplacian smoothing has been applied by the state-of-the-art works to learn a more robust node representation, these works cannot adapt to the topological characteristics of different networks, thereby causing the new oversmoothing problem and reducing the performance on some networks. In contrast, we adopt a smoothing parameter that is evaluated from the topological characteristics of a specified network, such as small worldness or node convergency and, thus, can smooth the nodes' attribute and structure information adaptively and derive both robust and distinguishable node features for different networks. Moreover, we develop an integrated autoencoder to learn the node representation by reconstructing the combination of the smoothed structure and attribute information. By observation of extensive experiments, our approach can preserve the intrinsical information of networks more effectively than the state-of-the-art works on a number of benchmark datasets with very different topological characteristics.

With the increase of private cars, traditional traffic signal control methods cannot alleviate the traffic congestion problem. Reinforcement learning (RL) is increasingly used in adaptive traffic light control. As urban traffic becomes more complex, reinforcement learning algorithms solely based on value or policy are not suitable for such scenarios. Moreover, the centralized method does not show a good effect on the multi-intersection, in particular, when the traffic flow is in the high-dimensional continuous state space. In this article, we propose a decentralized framework based on the advantage actor-critic (A2C) algorithm by assigning global control to each local RL agent or intersection. A2C algorithm involved in this article is a product of combining policy function and value function, which has good convergence ability and can be applied to continuous state space. The decentralized methods may put a new challenge: from the perspective of each local agent, the environment becomes partially observable. We overcome this problem by putting forward a region-aware cooperative strategy (RACS) based on graph attention network (GAT), which can incorporate the spatial information of the surrounding agents. We carry out experiments on synthetic traffic grid and real-world traffic network of Monaco city to compare with the existing A2C and Q-learning algorithms. Experimental results confirm that our RACS method has a shorter queue length and less waiting time than the two existing algorithms, and can reduce the total vehicle travel time.

Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents with fixed routes and schedules, which leads to latent travel regularity. However, human mobility patterns, specifically the complex relationships between bus passengers, are deeply hidden in this fixed mobility mode. Although many models exist to predict traffic flow, human mobility patterns have not been well explored in this regard. To address this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN). Firstly, we construct a novel sharing-stop network to model relationships between passengers based on bus record data. Then, we employ GCN to extract features from the graph by learning useful topology information and introduce a deep clustering method to recognize mobility patterns hidden in bus passengers. Furthermore, to fully utilize spatio-temporal information, we propose GCN2Flow to predict passenger flow based on various mobility patterns. To the best of our knowledge, this paper is the first work to adopt a multi-pattern approach to predict the bus passenger flow by taking advantage of graph learning. We design a case study for optimizing routes. Extensive experiments upon a real-world bus dataset demonstrate that MPGCN has potential efficacy in passenger flow prediction and route optimization.

Influence maximization has recently received significant attention for scheduling online campaigns or advertisements on social network platforms. However, most studies only focus on user influence via cyber interactions while ignoring their physical interactions which are also essential to gauge influence propagation. Additionally, targeted campaigns or advertisements have not received sufficient attention. To address these issues, we first devise a novel holistic influence diffusion model that takes into account both cyber and physical user interactions in an effective and practical way. Based on the new diffusion model, we formulate a new problem ofholistic influence maximization, denoted asHIMquery, for targeted advertisements in a spatial social network. TheHIMquery problem aims to find a minimum set of users whose holistic influence can cover all target users in the network, which belongs to a set covering problem. Since theHIMquery problem is NP-hard, we develop a greedy baseline algorithm and then improve on this algorithm to reduce the computational cost. To deal with large networks, we also design a spatial-social index to maintain the social, spatial and textual information of users, as well as developing an index-based efficient solution. Finally, we conduct extensive experiments using one synthetic and three real-world datasets to validate the efficiency and effectiveness of the proposed holistic influence diffusion model and our developed algorithms.

The outbreak of the novel coronavirus disease (COVID-19) has been ongoing for almost two years and has had an unprecedented impact on the daily lives of people around the world. More recently, the emergence of the Delta variant of COVID-19 has once again put the world at risk. Fortunately, many countries and companies have developed vaccines for the coronavirus. As of 23 August 2021, more than 20 vaccines have been approved by the World Health Organization (WHO), bringing light to people besieged by the pandemic. The global rollout of the COVID-19 vaccine has sparked much discussion on social media platforms, such as the effectiveness and safety of the vaccine. However, there has not been much systematic analysis of public opinion on the COVID-19 vaccine. In this study, we conduct an in-depth analysis of the discussions related to the COVID-19 vaccine on Twitter. We analyze the hot topics discussed by people and the corresponding emotional polarity from the perspective of countries and vaccine brands. The results show that most people trust the effectiveness of vaccines and are willing to get vaccinated. In contrast, negative tweets tended to be associated with news reports of post-vaccination deaths, vaccine shortages, and post-injection side effects. Overall, this study uses popular Natural Language Processing (NLP) technologies to mine people's opinions on the COVID-19 vaccine on social media and objectively analyze and visualize them. Our findings can improve the readability of the confusing information on social media platforms and provide effective data support for the government and policy makers.

The arrival of 5G networks has extensively promoted the growth of content delivery services (CDSs). Understanding and predicting the spatio-temporal distribution of CDSs are beneficial to mobile users, Internet Content Providers and carriers. Conventional methods for predicting the spatio-temporal distribution of CDSs are mostly base-stations (BSs) centric, leading to weak generalization and spatio coarse-grained. To improve the spatio accuracy and generalization of modeling, we propose user-centric methods for CDSs spatio-temporal analysis. With geocoding and spatio-temporal graphs modeling algorithms, CDSs records collected from mobile devices are modeled as dynamic graphs with spatio-temporal attributes. Moreover, we propose a spatio-temporal-social multi-feature extraction framework for spatio fine-grained CDSs hot spots prediction. Specifically, an edge-enhanced graph convolutional block is designed to encode CDSs information based on the social relations and the spatio dependence features. Besides, we introduce the Long Short Term Memory (LSTM) to further capture the temporal dependence. Experiments on two real-world CDSs datasets verified the effectiveness of the proposed framework, and ablation studies are taken to evaluate the importance of each feature.

Knowledge Graph (KG) representation learning aims to encode both entities and relations into a continuous low-dimensional vector space. Most existing methods only concentrate on learning representations from structural triples in Euclidean space, which cannot well exploit the rich semantic information with hierarchical structure in KGs. In this paper, we propose a novel DataType-aware hyperbolic knowledge representation learning model called DT-GCN, which has the advantage of fully embedding attribute values of data types information. We refine data types into five primitive modalities, including integer, double, Boolean, temporal, and textual. For each modality, an encoder is specifically designed to learn its embedding. In addition, we define a unified space based on Euclidean, spherical, and hyperbolic space, which is a continuous curvature space that combines advantages of three different spaces. Extensive experiments on both synthetic and real-world datasets show that our model is consistently better than the state-of-the-art models. The average performance is improved by 2.19% and 3.46% than the optimal baseline model on node classification and link prediction tasks, respectively. The results of ablation experiments demonstrate the advantages of embedding data types information and leveraging the unified space.

Session-based recommendation is to predict an anonymous user's next action based on the user's historical actions in the current session. However, the cold-start problem of limited number of actions at the beginning of an anonymous session makes it difficult to model the user's behavior, i.e., hard to capture the user's various and dynamic preferences within the session. This severely affects the accuracy of session-based recommendation. Although some existing meta-learning based approaches have alleviated the cold-start problem by borrowing preferences from other users, they are still weak in modeling the behavior of the current user. To tackle the challenge, we propose a novel cluster-based meta-learning model for session-based recommendation. Specially, we adopt a soft-clustering method and design a parameter gate to better transfer shared knowledge across similar sessions and preserve the characteristics of the session itself. Besides, we apply two self-attention blocks to capture the transition patterns of sessions in both item and feature aspects. Finally, comprehensive experiments are conducted on two real-world datasets and demonstrate the superior performance of CBML over existing approaches.

Next Point-of-Interest (POI) recommendation plays an important role in location-based services. The state-of-the-art methods utilize recurrent neural networks (RNNs) to model users' check-in sequences and have shown promising results. However, they tend to recommend POIs similar to those that the user has often visited. As a result, users become bored with obvious recommendations. To address this issue, we propose Serendipity-oriented Next POI Recommendation model (SNPR), a supervised multi-task learning problem, with objective to recommend unexpected and relevant POIs only. To this end, we define the quantitativeserendipity as a trade-off ofrelevance andunexpectedness in the context of next POI recommendation, and design a dedicated neural network with Transformer to capture complex interdependencies between POIs in user's check-in sequence. Extensive experimental results show that our model can improverelevance significantly while theunexpectedness outperforms the state-of-the-art serendipity-oriented recommendation methods.

Probing techniques are widely used to identify faulty nodes in networks. Existing probe-based solutions for SDN fault localizationcan focus on two ways: per-rule and per-path. Both promote some certain switches to reporters by installing on them report rules. To avoid hindering other test packets, such report rules must vary between tests or be deleted before a next test, thus incurring excessive consumption on either TCAM resources of switches or bandwidth reserved for control messages. In this paper we present Voyager, a hybrid fault localization solution for SDN that fully combines the advantages of per-rule and per-path tests. Voyager significantly reduces the number of report rules and allows them to reside and function in switches persistently. With only one well-designed report rule for each switch installed, Voyager pinpoints faulty switches easily and tightly by sending test packets straight. Tests in Voyager are parallelizable and report rules are non-invasive. The performance evaluation on realistic datasets shows that Voyager is 24.0% to 92.3% faster than existing solutions.

Traffic light control is an extremely challenging problem in transportation. Recently, an increasing number of studies have employed reinforcement learning approaches to deal with traffic light control problems. However, these studies often observe the target intersection independently and ignore the dynamic effects from the surrounding intersections. Moreover, the dependence of historical information on current traffic conditions is also not fully exploited in multi-intersection traffic light control. In this work, we propose a spatial-temporal graph attention network based multi-agent deep Q-learning model. Our model can capture not only spatial features but also temporal features. Specifically, we first utilize the Graph Attention Network (GAT) to obtain the spatial relationship between the central intersection and surrounding intersections. Then Long Short-Term Memory (LSTM) network and the self-attention mechanism are used to get the time dimension information. Finally, we adopt a deep Q-learning network (DQN) to predict the action-value of each feasible action and choose the signal phase according to the size of the action-value. Experimental results on synthetic and realworld datasets confirm that our method can lead to both the least travel time and the maximum throughput, compared with some existing baselines.

With the networks becoming complex, traditional congestion control protocols face increasing challenges in providing high-quality services for users. Traditional TCP and its variants fail to achieve high performance due to drawbacks in architectural design: predefined actions to specific network feedback. In this paper, we develop a learning-based TCP congestion control scheme RLCC, featuring a deep Q-network framework, in which senders learn the optimal control policies from observations instead of predefined rules. To apply DQN algorithms to congestion control problems, we first prove theoretically that congestion control problems are of Markov property. Therefore, the model-free reinforcement learning algorithm DQN can be used to solve congestion control. This is because the application of DQN to the network congestion control problem is convergent, and there exists an optimal strategy to obtain the best action for congestion control. We improved the network's performance by carefully designing the reward function and choosing the appropriate form and parameters through extensive experimentation. Extensive experiments on real-world environments of Pantheon via AWS confirm that RLCC can achieve utilization improvements over the traditional TCP congestion control schemes with higher throughput and lower transmission latency, and outperform the recently proposed learning-based congestion control protocol.

Given a bipartite graph, the maximum balanced biclique (MBB) problem, discovering a mutually connected while disjoint sets of equal size with the maximum cardinality, plays a significant role for mining the bipartite graph and has numerous applications. Despite the NP-hardness of the MBB problem, in this paper, we show that an exact MBB can be discovered extremely fast in bipartite graphs for real applications. We propose two exact algorithms dedicated for small dense and large sparse bipartite graphs respectively. For dense bipartite graphs, an O*(1.3803n) algorithm is proposed. This algorithm in fact can find an MBB very fast for small dense bipartite graphs that are common for applications such as VLSI design. This is because, using our proposed novel techniques, the search can fast converge to sufficiently dense bipartite graphs which we prove to be polynomial-time solvable. For large sparse bipartite graphs typical for applications such as biological data analysis, an O*(1.3803 δ) algorithm is proposed, where δ is only a few hundred for large sparse bipartite graphs with millions of vertices. The indispensible optimization that leads to this time complexity is: we transform a large sparse bipartite graph into a limited number of dense subgraphs such that each of the dense subgraphs has up to δ vertices and then apply our proposed algorithm for dense bipartite graphs on each of the subgraphs. To further speed up this algorithm, tighter upper bounds, faster heuristics and more effective reductions are proposed, allowing an MBB to be discovered within a few seconds for bipartite graphs with millions of vertices. Extensive experiments are conducted on synthetic and real large bipartite graphs to demonstrate the efficiency and effectiveness of our proposed algorithms and techniques.

The papers in this special section focus on advanced complex data analytics for smart city industrial environments. With the continuous increase in size of populations living in cities, those residents face increasing environmental pressures and infrastructure needs. To deliver a better quality of life for these residents to address these demands at a sustainable cost, smart technologies can help cities meet these challenges, and have been become the next wave of public investment. It all starts with data to be generated by the residents living in the cities. This special section will focus on the ever-increasing challenges of big complex data like social network data, traffic network data, and IoT network data altogether in the industrial environment of smart city.

With the proliferation of mobile device users, the Device-to-Device (D2D) communication has ascended to the spotlight in social network for users to share and exchange enormous data. Different from classic online social network (OSN) like Twitter and Facebook, each single data file to be shared in the D2D social network is often very large in data size, e.g., video, image or document. Sometimes, a small number of interesting data files may dominate the network traffic, and lead to heavy network congestion. To reduce the traffic congestion and design effective caching strategy, it is highly desirable to investigate how the data files are propagated in offline D2D social network and derive the diffusion model that fits to the new form of social network. However, existing works mainly concern about link prediction, which cannot predict the overall diffusion path when network topology is unknown. In this article, we propose D2D-LSTM based on Long Short-Term Memory (LSTM), which aims to predict complete content propagation paths in D2D social network. Taking the current user's time, geography and category preference into account, historical features of the previous path can be captured as well. It utilizes prototype users for prediction so as to achieve a better generalization ability. To the best of our knowledge, it is the first attempt to use real world large-scale dataset of mobile social network (MSN) to predict propagation path trees in a top-down order. Experimental results corroborate that the proposed algorithm can achieve superior prediction performance than state-of-the-art approaches. Furthermore, D2D-LSTM can achieve 95% average precision for terminal class and 17% accuracy for tree path hit.

SimRank is a significant metric to measure the similarity of nodes in graph data analysis. The problem of SimRank computation has been studied extensively, however there is no existing work that can provide one unified algorithm to support the SimRank computation both on static and temporal graphs. In this work, we first propose CrashSim, an index-free algorithm for single-source SimRank computation in static graphs. CrashSim can provide provable approximation guarantees for the computational results in an efficient way. In addition, as the reallife graphs are often represented as temporal graphs, CrashSim enables efficient computation of SimRank in temporal graphs. We formally define two typical SimRank queries in temporal graphs, and then solve them by developing an efficient algorithm based on CrashSim, called CrashSim-T. From the extensive experimental evaluation using five real-life and synthetic datasets, it can be seen that the CrashSim algorithm and CrashSim-T algorithm substantially improve the efficiency of the state-of-the-art SimRank algorithms by about 30%, while achieving the precision of the result set with about 97%.

User engagement has recently received significant attention in understanding decay and expansion of communities in social networks. However, the problem of user engagement hasn't been fully explored in terms of users' specific interests and structural cohesiveness altogether. Therefore, we fill the gap by investigating the problem of community engagement from the perspective of attributed communities. Given a set of keywords W, a structure cohesive parameter k, and a budget parameter l, our objective is to find l number of users who can induce a maximal expanded community. Meanwhile, every community member must contain the given keywords in W and the community should meet the specified structure cohesiveness constraint k. We introduce this problem as best-Anchored Vertex set Exploration (AVE).To solve the AVE problem, we develop a Filter-Verify framework by maintaining the intermediate results using multiway tree, and probe the best anchored users in a best search way. To accelerate the efficiency, we further design a keyword-aware anchored and follower index, and also develop an index-based efficient algorithm. The proposed algorithm can greatly reduce the cost of computing anchored users and their followers. Additionally, we present two bound properties that can guarantee the correctness of our solution. Finally, we demonstrate the efficiency of our proposed algorithms and index. We measure the effectiveness of attributed community-based community engagement model by conducting extensive experiments on five real-world datasets.

With the boom in cloud computing, data outsourcing in location-based services is proliferating and has attracted increasing interest from research communities and commercial applications. Nevertheless, since the cloud server is probably both untrusted and malicious, concerns of data security and result integrity have become on the rise sharply. However, there exist little work that can commendably assure the data security and result integrity using a unified way. In this paper, we study the problem of secure and verifiable k nearest neighbor query (SVkNN). To support SVkNN, we first propose a novel unified structure, called verifiable and secure index (VSI). Based on this, we devise a series of secure protocols to facilitate query processing and develop a compact verification strategy. Given an SVkNN query, our proposed solution can not merely answer the query efficiently while can guarantee: 1) preserving the privacy of data, query, result and access patterns; 2) authenticating the correctness and completeness of the results without leaking the confidentiality. Finally, the formal security analysis and complexity analysis are theoretically proven and the performance and feasibility of our proposed approaches are empirically evaluated and demonstrated.

Representation learning of knowledge graphs aims to project both entities and relations as vectors in a continuous low-dimensional space. Relation Hierarchical Structure (RHS), which is constructed by a generalization relationship named subRelationOf between relations, can improve the overall performance of knowledge representation learning. However, most of the existing methods ignore this critical information, and a straightforward way of considering RHS may have a negative effect on the embeddings and thus reduce the model performance. In this paper, we propose a novel method named TransRHS, which is able to incorporate RHS seamlessly into the embeddings. More specifically, TransRHS encodes each relation as a vector together with a relation-specific sphere in the same space. Our TransRHS employs the relative positions among the vectors and spheres to model the subRelationOf, which embodies the inherent generalization relationships among relations. We evaluate our model on two typical tasks, i.e., link prediction and triple classification. The experimental results show that our TransRHS model significantly outperforms all baselines on both tasks, which verifies that the RHS information is significant to representation learning of knowledge graphs, and TransRHS can effectively and efficiently fuse RHS into knowledge graph embeddings.

Geo-social group search aims to find a group of people proximate to a location while socially related. One of the driven applications for geo-social group search is organizing an impromptu activity. This is because the social cohesiveness of a found geo-social group ensures a good communication atmosphere for the activity and the spatial closeness of the geo-social group reduces the preparation time for the activity. Most existing works treat geo-social group search as a problem that finds a group satisfying a single social constraint while optimizing the spatial proximity. However, since different impromptu activities have diverse demands on attendees, e.g. an activity could require (or prefer) the attendees to have skills (or favorites) related to the activity, the existing works cannot find this kind of geo-social groups effectively. In this paper, we propose a novel geo-social group model, equipped with elegant keyword constraints, to fill this gap. We propose a novel search framework which first significantly narrows down the search space with theoretical guarantees and then efficiently finds the optimum result. To evaluate the effectiveness, we conduct experiments on real datasets, demonstrating the superiority of our proposed model. We conduct extensive experiments on large semi-synthetic datasets for justifying the efficiency of the proposed search algorithms.

Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this article, we propose a novel embedding algorithm that incorporates network motifs to capture higher order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms (by 20%) and the state-of-the-art embedding-based algorithms (by 19%).

Mobile crowdsourcing (MCS), as an effective and crucial technique of Industrial Internet of Things, is enabling smart city initiatives in the real world. It aims at incorporating the intelligence of dynamic crowds to collect and compute decentralized ubiquitous sensing data that can be used to solve major urbanization problems such as traffic congestion. The shared bus, as a neotype transportation mode, aims at improving the resource utilization rate and maintaining the advantages of convenience and economy. In this article, we provide a scheme to profile shared buses through heterogeneous mobile crowdsourced data (TRProfiling). First, we design an MCS-based shared bus data generation and collection solution to overcome the aforementioned data scarcity issue. Then, we propose a travel profiling to profile resident travel and design a method called multiconstraint evolution algorithm to optimize the routes. Experimental results demonstrate that TRProfiling has an excellent performance in satisfying passengers’ travel requirements.

Community search on attributed networks has recently attracted great deal of research interest. However, most of existing works require query users to specify some community structure parameters. This may not be always practical as sometimes a user does not have the knowledge and experience to decide the suitable parameters. In this paper, we propose a novel parameter-free contextual community model for attributed community search. The proposed model only requires a query context, i.e., a set of keywords describing the desired matching community context, while the community returned is both structure and attribute cohesive w.r.t. the provided query context. We theoretically show that both our exact and approximate contextual community search algorithms can be executed in worst case polynomial time. The exact algorithm is based on an elegant parametric maximum flow technique and the approximation algorithm that significantly improves the search efficiency is analyzed to have an approximation factor of 1/3. In the experiment, we use six real networks with ground-truth communities to evaluate the effectiveness of our contextual community model. Experimental results demonstrate that the proposed model can find near ground-truth communities. We also test both our exact and approximate algorithms using eight large real networks to demonstrate the high efficiency of the proposed algorithms.

In recent years, spatial keyword query has attracted wide-spread research attention due to the popularity of the location-based services. To efficiently support the online spatial keyword query processing, the data owners need to outsource their data and the query processing service to cloud platforms. However, the outsourcing services may raise privacy leaking issues because the cloud server on the platforms may not be trusted for both data owners and query users. Therefore, in this work, we first propose and formalize the problem of privacy-preserving boolean spatial keyword query under the widely accepted Known Background Thread Model. And then, we devise a novel privacy-preserving spatial-textual Bloom Filter encoding structure and an encrypted R-tree index. They can maintain both spatial and text information together in a secure way while answering the encrypted spatial keyword queries without the need for data decryption. To further accelerate the query processing, a compressed encrypted index is provided to deal with the challenges of the large dimension expansion and the expensive space consumption in the encrypted R-tree index. In addition, we develop the corresponding algorithms based on the designed index, and present the in-depth security analysis to show our work's satisfaction meeting the strong secure scheme. Finally, we demonstrate the performance of our proposed index and algorithms by conducting extensive experiments on four datasets under various system settings.

With the popularity of knowledge graphs growing rapidly, large amounts of RDF graphs have been released, which raises the need for addressing the challenge of distributed subgraph matching queries. In this paper, we propose an efficient distributed method to answer subgraph matching queries on big RDF graphs using MapReduce. In our method, query graphs are decomposed into a set of stars that utilize the semantic and structural information embedded RDF graphs as heuristics. Two optimization techniques are proposed to further improve the efficiency of our algorithms. One algorithm, called RDF property filtering , filters out invalid input data to reduce intermediate results; the other is to improve the query performance by postponing the Cartesian product operations. The extensive experiments on both synthetic and real-world datasets show that our method outperforms the close competitors S2X and SHARD by an order of magnitude on average.

Usage of smart meters (SMs) have significantly increased in the recent days due to the advantages they offer. However, it is possible for an adversary to extract private information about a consumer by observing the SM readings. Therefore, it is important to protect the privacy of consumers using SMs. Among the SM privacy protection mechanisms, rechargeable battery (RB)-based mechanisms are preferred as they do not alter SM readings. The existing mechanisms cannot protect the privacy when the consumer energy usage is either low or high for a longer period. Furthermore, these mechanisms do not perform well in online scenario where the energy management unit (EMU) only knows the current and past consumer energy demands. To solve this problem, in this article, we proposed a novel online privacy protection mechanism to protect the privacy of SMs using a progressive average-based algorithm (PABA). The proposed PABA uses two uniquely designed algorithms to ensure protection of privacy and energy cost reduction during peak and off-peak periods. Moreover, an adaptive output smoothing technique is used to further enhance the privacy. Compared with the privacy protection mechanisms designed for tackling SM privacy, the proposed mechanism achieves higher amount of privacy while reducing the energy cost. The validity of the proposed privacy enhancement mechanism is demonstrated by simulation results.

Public transport is of great significance in megacities. Transit-oriented development (TOD) has become a reliable solution to urban sustainable development, which can reshape the urban form and improve its quality. This paper focuses on leveraging heterogeneous mega urban data to answer three critical questions in TOD: what region looks like under TOD concept, which regions have the potential to be TOD regions, and how to construct these TOD regions. For region partition, we propose a connected component-based clustering algorithm, which merges the large amount of public transport stops into representative cluster ones as region centers, and then apply the Voronoi algorithm to locate the region boundaries according to the cluster centers. For TOD region identification, we present a link importance-based random walk method that considers the importance of various transits and further identifies the most valuable regions to be TOD. For discovering functions of TOD regions, we introduce a multifactor-based function characterization approach that combines both the static linguistic factor and human mobility factor together and then derives the actual function distributions. The experiments, which are conducted on three real data sets, show the superiority of the proposed methods to solve the problems of region partition, TOD region identification, and function characterization for the megacities. In the meantime, the results provide support for the government to formulate public policy to construct a TOD city.

Vision-based automatic counting of people has widespread applications in intelligent transportation systems, security, and logistics. However, there is currently no large-scale public dataset for benchmarking approaches on this problem. This paper fills this gap by introducing the first real-world RGB-D people counting dataset (PCDS) containing over 4500 videos recorded at the entrance doors of buses in normal and cluttered conditions. It also proposes an efficient method for counting people in real-world cluttered scenes related to public transportations using depth videos. The proposed method computes a point cloud from the depth video frame and re-projects it onto the ground plane to normalize the depth information. The resulting depth image is analyzed for identifying potential human heads. The human head proposals are meticulously refined using a 3D human model. The proposals in each frame of the continuous video stream are tracked to trace their trajectories. The trajectories are again refined to ascertain reliable counting. People are eventually counted by accumulating the head trajectories leaving the scene. To enable effective head and trajectory identification, we also propose two different compound features. A thorough evaluation on PCDS demonstrates that our technique is able to count people in cluttered scenes with high accuracy at 45 fps on a 1.7-GHz processor, and hence it can be deployed for effective real-time people counting for intelligent transportation systems.

Structural clustering is a fundamental graph mining operator which is not only able to find densely-connected clusters, but it can also identify hub vertices and outliers in the graph. Previous structural clustering algorithms are tailored to deterministic graphs. Many real-world graphs, however, are not deterministic, but are probabilistic in nature because the existence of the edge is often inferred using a variety of statistical approaches. In this paper, we formulate the problem of structural clustering on probabilistic graphs, with the aim of finding reliable clusters in a given probabilistic graph. Unlike the traditional structural clustering problem, our problem relies mainly on a novel concept called reliable structural similarity which measures the probability of the similarity between two vertices in the probabilistic graph. We develop a dynamic programming algorithm with several powerful pruning strategies to efficiently compute the reliable structural similarities. With the reliable structural similarities, we adapt an existing solution framework to calculate the structural clustering on probabilistic graphs. Comprehensive experiments on five real-life datasets demonstrate the effectiveness and efficiency of the proposed approaches.

The problem of influence maximization has recently received significant attention. However, most studies focused on user influence via cyber interactions while ignoring their physical interactions which are important to gauge influence propagation. Additionally, targeted campaigns or advertisements have not received sufficient attention. To do this, we first devise a novel holistic influence diffusion model and then formulate a new holistic influence maximization query problem and develop three algorithms. Finally, we conduct extensive experiments to evaluate the effectiveness and efficiency of the proposed solutions.

The problem of influence maximization has attracted a lot of attention as it provides a way to improve marketing, branding, and product adoption. However, existing studies rarely consider the physical locations of the social users, although location is an important factor in targeted marketing. In this paper, we investigate the problem of influence spanning maximization in location-aware social networks. Our target is to identify the maximum spanning geographical regions in a query region, which is very different from the existing methods that focus on the quantity of the activated users in the query region. Since the problem is NP-hard, we develop one greedy algorithm with a 1-1/e approximation ratio and further improve its efficiency by developing an upper bound based approach. Then, we propose the OIR index by combining ordered influential node lists and an R*-tree and design the index based solution. The efficiency and effectiveness of our proposed solutions and index have been verified using three real datasets.

We are pleased to present a special issue of Data Science and Engineering (DSE), which contains a collection of five extended papers from the DASFAA 2018 conference.Besides these five papers, this DSE issue also has one invited paper.DASFAA 2018 is the 23rd International Conference on Database Systems for Advanced Applications.DASFAA is an annual international database conference, which provides a forum for technical presentations and discussions among database researchers, developers, and users from academia, business, and industry.This year the dominant topics for the selected papers included learning models, graph and network data processing, and social network analysis, followed by text and data mining, recommendation, data quality and crowd sourcing, and trajectory and stream data.Selected papers also included topics relating to network embedding, sequence and temporal data processing, RDF and knowledge graphs, security and privacy, medical data mining, query processing and optimization, search and information retrieval, multimedia data processing, and distributed computing.The 2018 edition of DASFAA was held in Gold Coast, Australia, and attracted a total of 360 research paper submissions, spanning over numerous active and emerging topic areas.The conference program committee selected 83 full research papers and 21 short papers, six industry papers, and eight demo papers to be presented at the conference and published in the conference proceedings [1,2].The conference program also included keynote presentations by Dr.

The problem of k-truss search has been well defined and investigated to find the highly correlated user groups in social networks. But there is no previous study to consider the constraint of users' spatial information in k-truss search, denoted as co-located community search in this paper. The co-located community can serve many real applications. To search the maximum co-located communities efficiently, we first develop an efficient exact algorithm with several pruning techniques. After that, we further develop an approximation algorithm with adjustable accuracy guarantees and explore more effective pruning rules, which can reduce the computational cost significantly. To accelerate the real-time efficiency, we also devise a novel quadtree based index to support the efficient retrieval of users in a region and optimise the search regions with regards to the given query region. Finally, we verify the performance of our proposed algorithms and index using five real datasets.

A socio spatial group query finds a group of users who possess strong social connections with each other and have the minimum aggregate spatial distance to a meeting point. Existing studies limit to either finding the best group of a fixed size for a single meeting location, or a single group of a fixed size w.r.t. multiple locations. However, it is highly desirable to consider multiple locations in a real-life scenario in order to organize impromptu activities of groups of various sizes. In this paper, we propose Top k Flexible Socio Spatial Group Query (Top k-FSSGQ) to find the top k groups w.r.t. multiple POIs where each group follows the minimum social connectivity constraints. We devise a ranking function to measure the group score by combining social closeness, spatial distance, and group size, which provides the flexibility of choosing groups of different sizes under different constraints. To effectively process the Top k-FSSGQ, we first develop an Exact approach that ensures early termination of the search based on the derived upper bounds. We prove that the problem is NP-hard, hence we first present a heuristic based approximation algorithm to effectively select members in intermediate solution groups based on the social connectivity of the users. Later we design a Fast Approximate approach based on the relaxed social and spatial bounds, and connectivity constraint heuristic. Experimental studies have verified the effectiveness and efficiency of our proposed approaches on real datasets.

Social media community detection is a fundamental challenge in social data analytics, in order to understand user relationships and improve social recommendations. Although the problem has been extensively investigated, the majority of research has been based on social networks with static structures. Our findings within large social networks, such as Twitter, show that only a few users have interactions or communications at fixed time intervals. Finding active communities that demonstrate constant interactions between its members comprises a reasonable perspective. Communities examined from this perspective will provide time-variant social relationships, which may greatly improve the applicability of social data analytics. In this paper, we address the problem of temporal interaction-biased community detection using a four-step process. First, we develop a partition approach using an objective function based on clique structure, to enhance the time efficiency of our methodology. Second, we develop an influence propagation model that gives greatest weight to active edges or to inactive edges in close proximity to active edges. Third, we develop expansion-driven algorithms to efficiently find the activity-biased densest community. Finally, we verify the effectiveness of the extended community metric and the efficiency of the algorithms using three real data sets and a case study conducted on Twitter dynamic data set.

Social networks have become a vital mechanism to disseminate information to friends and colleagues. But the dynamic nature of information and user connectivity within these networks raised many new and challenging research problems. One of them is the query-related topic search in social networks. In this work, we investigate the important problem of the personalized influential topic search. There are two challenging questions that need to be answered: how to extract the social summarization of the social network so as to measure the topics' influence at the similar granularity scale? and how to apply the social summarization to the problem of personalized influential topic search. Based on the evaluation using real-world datasets, our proposed algorithms are proved to efficient and effective.

Detecting social communities in large social networks provides an effective way to analyze the social media users' behaviors and activities. It has drawn extensive attention from both academia and industry. One essential aspect of communities in social networks is outer influence which is the capability to spread internal information of communities to external users. Detecting the communities of high outer influence has particular interest in a wide range of applications, e.g., Ads trending analytics, social opinion mining and news propagation pattern discovery. However, the existing detection techniques largely ignore the outer influence of the communities. To fill the gap, this work investigates the Most Influential Community Search problem to disclose the communities with the highest outer influences. We firstly propose a new community model, maximal kr-Clique community, which has desirable properties, i.e., society, cohesiveness, connectivity, and maximum. Then, we design a novel tree-based index structure, denoted as C-Tree, to maintain the offline computed r-cliques. To efficiently search the most influential communities, we also develop four advanced index-based algorithms which improve the search performance of non-indexed solution by about 200 times. The efficiency and effectiveness of our solution have been extensively verified using six real datasets and a small case study.

Influence maximization is a recent but well-studied problem which helps identify a small set of users that are most likely to “influence” the maximum number of users in a social network. The problem has attracted a lot of attention as it provides a way to improve marketing, branding, and product adoption. However, existing studies rarely consider the physical locations of the users, but location is an important factor in targeted marketing. In this paper, we propose and investigate the problem of influence maximization in location-aware social networks, or, more generally,Geo-social Influence Spanning Maximization. Given a query$q$composed of a region$R$, a regional acceptance rate$\rho$, and an integer$k$as a seed selection budget, our aim is to find the maximum geographic spanning regions (MGSR). We refer to this as the MGSR problem. Our approach differs from previous work as we focus more on identifying the maximum spanning geographical regions within a region$R$, rather than just the number of activated users in the given network like the traditional influence maximization problem[14]. Our research approach can be effectively used for online marketing campaigns that depend on the physical location of social users. To address the MGSR problem, we first prove NP-Hardness. Next, we present a greedy algorithm with a$1-1/e$approximation ratio to solve the problem, and further improve the efficiency by developing an upper bounded pruning approach. Then, we propose the OIR*-Tree index, which is a hybrid index combining ordered influential node lists with an R*-tree. We show that our index based approach is significantly more efficient than the greedy algorithm and the upper bounded pruning algorithm, especially when$k$is large. Finally, we evaluate the performance for all of the proposed approaches using three real datasets.

Recently, answering keyword queries on graph data has drawn a great deal of attention from database communities. However, most graph keyword search solutions proposed so far primarily focus on a single query setting. We observe that for a popular keyword query system, the number of keyword queries received could be substantially large even in a short time interval, and the chance that these queries share common keywords is quite high. Therefore, answering keyword queries in batches would significantly enhance the performance of the system. Motivated by this, this paper studies efficient batch processing for multiple keyword queries on graph data. Realized that finding both the optimal query plan for multiple queries and the optimal query plan for a single keyword query on graph data are computationally hard, we first propose two heuristic approaches which target maximizing keyword overlap and give preferences for processing keywords with short sizes. Then we devise a cardinality based cost estimation model that takes both graph data statistics and search semantics into account. Based on the model, we design an A* based algorithm to find the global optimal execution plan for multiple queries. We evaluate the proposed model and algorithms on two real datasets and the experimental results demonstrate their efficacy.

Graph data management and matching similar graphs are very important for many applications including bioinformatics, computer vision, VLSI design, bug localization, road networks, social and communication networking. Many graph indexing and similarity matching techniques have already been proposed for managing and querying graph data. In similar graph matching, a user is returned with the database graphs whose distances with the query graph are below a threshold. In such query settings, a user may not receive certain database graphs that are very similar to the query graph if the initial query graph is inappropriate/imperfect for the expected answer set. To exemplify this, consider a drug designer who is looking for chemical compounds that could be the target of her hypothetical drug before realizing it. In response to her query, the traditional search system may return the structures from the database that are most similar to the query graph. However, she may get surprised if some of the expected targets are missing in the answer set. She may then seek assistance from the system by asking “Is there other query graph that can match my expected answer set?”. The system may then modify her initial query graph to include the missing answers in the new answer set. Here, we study this kind of problem of answering why-not questions in similar graph matching for graph databases.

It has been estimated that the cumulative sales of Electric Vehicles (EVs) will be up to 5.9 million and the stock of EVs will be up to 20 million by 2020 [1]. As the number of EVs is expanding, there is a growing need for widely distributed, publicly accessible, EV charging facilities. The public EV Chargers (EVCs) are expected to be found and will be needed where there is on-street parking, at taxi stands, in parking lots at places of employment, hotels, airports, shopping centres, convenience shops, fast food restaurants, and coffee houses, etc. In this work, we aim to optimize the distribution of public EVCs across the city such that (i) the overall revenue generated by the EVCs is maximized, subject to (ii) the overall driver discomfort (e.g., queueing time) for EV charging is minimized. This is the first study on EVC distribution where EVCs are assumed to be installed in almost all regions across a city. The problem is formulated using a bilevel optimization model. We propose an alternating framework to solve it and have proved that a local minima is achievable. Moreover, this work introduces novel methods to extract information to understand the discomfort of petroleum car drivers, EV charging demands, parking time and parking fees across the city. The source data explored include the trajectories of taxis, the distribution of petroleum stations and various local features. The empirical study uses the real data sets from Shenzhen City, one of the largest cities in China. The extensive tests verify the superiority of the proposed bilevel optimization model in all aspects.

The study of urban networks reveals that the accessibility of important city objects for the vehicle traffic and pedestrians is significantly correlated to the popularity, micro-criminality, micro-economic vitality, and social liveability of the city, and is always the chief factor in regulating the growth and expansion of the city. The accessibility between different components of an urban structure are frequently measured along the streets and routes considered as edges of a planar graph, while the traffic ultimate destination points and street junctions are treated as vertices. For estimation of the accessibility of destination vertex$j$from vertex$i$through urban networks, in particular, the random walks are used to calculate the expected distance a random walker starting from$i$makes before$j$is visited (known asaccess time). The state-of-the-art of access time computation is costly in large planar graphs since it involves matrix operation over entire graph. The time complexity is$O(n^{2.376})$where$n$is the number of vertices in the planar graph. To enable efficient access time query answering in large planar graphs, this work proposes the first access time oracle which is based on the proposed access time decomposition and reconstruction scheme. The oracle is a hierarchical data structure with deliberate design on the relationships between different hierarchical levels. The storage requirement of the proposed oracle is$O(n^{\frac{4}{3}}\log \log n)$and the access time query response time is$O(n^{\frac{2}{3}})$. The extensive tests on a number of large real-world road networks (with up to about 2 million vertices) have verified the superiority of the proposed oracle.

Social networks are a vital mechanism to disseminate information to friends and colleagues. In this work, we investigate an important problem—thepersonalized influential topic search, or PIT-Search in a social network: Given a keyword query$q$issued by a user$u$in a social network, a PIT-Search is to find the top-$k$$q$-related topics that are most influential for the query user$u$. The influence of a topic to a query user depends on the social connection between the query user and the social users containing the topic in the social network. To measure the topics’ influence at the similar granularity scale, we need to extract the social summarization of the social network regarding topics. To make effective topic-aware social summarization, we propose two random-walk based approaches: random clustering and an L-length random walk. Based on the proposed approaches, we can find a small set of representative users with assigned influential scores to simulate the influence of the large number of topic users in the social network with regards to the topic. The selected representative users are denoted as the social summarization of topic-aware influence spread over the social network. And then, we verify the usefulness of the social summarization by applying it to the problem of personalized influential topic search. Finally, we evaluate the performance of our algorithms using real-world datasets, and show the approach is efficient and effective in practice.

Given a keyword query q and a large structured, traditional keyword search may return a large number of relevant results to users, which leads to a frustrating procedure for the users to select their interesting results. To help users understand the data to be searched, in this work we investigate the problem of frequent co-occurring terms (FCTs) in large relational data. By returning a set of most FCTs with the given keywords, we can provide a chance for users to see a big picture of relevant data information. The investigation of FCT problem is also one of the fundamental building blocks of data mining because the discovered FCTs can be employed to analyze the topics or contexts of user interest. Although the problem of FCTs computation was proposed and investigated in Tao and Yu [(2009) Finding Frequent Co-Occurring Terms in Relational Keyword Search. 12th Int. Conf. Extending Database Technology EDBT, Saint-Petersburg, Russia, March 23–26, pp. 839–850. ACM, New York, USA], further investigation is needed to improve the performance because FCT computation is very expensive. Especially for the increasing volume of data, the centralized approach in Tao and Yu [(2009) Finding Frequent Co-Occurring Terms in Relational Keyword Search. 12th Int. Conf. Extending Database Technology EDBT, Saint-Petersburg, Russia, March 23–26, pp. 839–850. ACM, New York, USA] may incur a big challenge on the efficiency of performing an FCT computation. To do this, we investigate how to perform parallel FCT computation using MapReduce which is a well-accepted framework for data-intensive applications over clusters of computers. We design an effective mapping mechanism that exploits the approximately maximal workload of FCT computation for balancing the computational cost of each processor, while reducing the shuffling cost and avoiding the data-skewness. Analytical and experimental evaluations demonstrate the efficiency and scalability of our proposed approach using TPC-H benchmark datasets with different sizes.

Answeringwhy-notquestions in databases is promised to have wide application prospect in many areas and thereby, has attracted recent attention in the database research community. This paper addresses the problem of answering these so-calledwhy-notquestions in similar graph matching for graph databases. Given a set of answer graphs of an initial query graph$q$and a set of missing (why-not) graphs, we aim to modify$q$into a new query graph$q^*$such that the missing graphs are included in the new answer set of$q^*$. We present an approximate solution to address the above as the optimal solution is NP-hard to compute. In our approach, we first compute the bounded search space and the distance to be minimized for$q^*$. Then, we present a two-phase algorithm to find the new query$q^*$. In the first phase, we generate a set of candidate edges to be added/deleted into/from the initial query$q$within the bounded search space and in the second phase, we select a subset of candidate edges generated in the first phase to minimize the distance for$q^*$. We also demonstrate the effectiveness and efficiency of our approach by conducting extensive experiments on two real datasets.

While keyword query empowers ordinary users to search vast amount of data, the ambiguity of keyword query makes it difficult to effectively answer keyword queries, especially for short and vague keyword queries. To address this challenging problem, in this paper we propose an approach that automatically diversifies XML keyword search based on its different contexts in the XML data. Given a short and vague keyword query and XML data to be searched, we first derive keyword search candidates of the query by a simple feature selection model. And then, we design an effective XML keyword search diversification model to measure the quality of each candidate. After that, two efficient algorithms are proposed to incrementally compute top-k qualified query candidates as the diversified search intentions. Two selection criteria are targeted: the k selected query candidates are most relevant to the given query while they have to cover maximal number of distinct results. At last, a comprehensive evaluation on real and synthetic data sets demonstrates the effectiveness of our proposed diversification model and the efficiency of our algorithms.

Recent years have witnessed an unprecedented proliferation of social media, e.g., millions of blog posts, micro-blog posts, and social networks on the Internet. This kind of social media data can be modeled in a large graph where nodes represent the entities and edges represent relationships between entities of the social media. Discovering keyword-based correlated networks of these large graphs is an important primitive in data analysis, from which users can pay more attention about their concerned information in the large graph. In this paper, we propose and define the problem of keyword-based correlated network computation over a massive graph. To do this, we first present a novel tree data structure that only maintains the shortest path of any two graph nodes, by which the massive graph can be equivalently transformed into a tree data structure for addressing our proposed problem. After that, we design efficient algorithms to build the transformed tree data structure from a graph offline and compute the γ-bounded keyword matched subgraphs based on the pre-built tree data structure on the fly. To further improve the efficiency, we propose weighted shingle-based approximation approaches to measure the correlation among a large number of γ-bounded keyword matched subgraphs. At last, we develop a merge-sort based approach to efficiently generate the correlated networks. Our extensive experiments demonstrate the efficiency of our algorithms on reducing time and space cost. The experimental results also justify the effectiveness of our method in discovering correlated networks from three real datasets.

We review the problem of finding contained rewritings (CRs) for XPath queries using XPath views. CR is proposed to cater for data integration scenarios, where views are unlikely to be complete due to the limited coverage of data sources, and hence equivalent rewritings are impossible to be found. As a result, we are usually required to find a maximal contained rewriting (MCR) for a query to provide the best possible answers. An MCR is a set of CRs, and may contain redundant CRs. Obviously, evaluating redundant CRs on materialized views is unnecessary. In this paper, we first address how to find the irredundant maximal contained rewriting (IMCR), i.e. all the irredundant CRs. We show that the existing approach ignores a type of situation, and turns out to be not sufficient. As a result, the only safe solution is a brute-force pairwise containment check for all the CRs. We then propose some heuristics to speed up the brute-force comparisons. When a materialized view is given, we propose how to evaluate the IMCR on the materialized view, which, to our knowledge, is the first work on optimizing the evaluation of a set of produced CRs on the materialized view by considering the inherent structural characteristics of the CRs. Our experiments show the effectiveness and efficiency of our algorithms.

The probabilistic threshold query is one of the most common queries in uncertain databases, where a result satisfying the query must be also with probability meeting the threshold requirement. In this paper, we investigate probabilistic threshold keyword queries (PrTKQ)over XML data, which is not studied before. We first introduce the notion of quasi-SLCA and use it to represent results for a PrTKQ with the consideration of possible world semantics. Then we design a probabilistic inverted (PI)index that can be used to quickly return the qualified answers and filter out the unqualified ones based on our proposed lower/upper bounds. After that, we propose two efficient and comparable algorithms: Baseline Algorithm and PI index-based Algorithm. To accelerate the performance of algorithms, we also utilize probability density function. An empirical study using real and synthetic data sets has verified the effectiveness and the efficiency of our approaches.

In this paper, we study how to find maximal k-edge-connected subgraphs from a large graph. k-edge-connected subgraphs can be used to capture closely related vertices, and finding such vertex clusters is interesting in many applications, e. g., social network analysis, bioinformatics, web link research. Compared with other explicit structures for modeling vertex clusters, such as quasi-clique, k-core, which only set the requirement on vertex degrees, k-edge-connected subgraph further requires high connectivity within a subgraph (a stronger requirement), and hence defines a more closely related vertex cluster.

Despite the proliferation of work on XML keyword query, it remains open to support keyword query over probabilistic XML data. Compared with traditional keyword search, it is far more expensive to answer a keyword query over probabilistic XML data due to the consideration of possible world semantics. In this paper, we firstly define the new problem of studying top-k keyword search over probabilistic XML data, which is to retrieve k SLCA results with the k highest probabilities of existence. And then we propose two efficient algorithms. The first algorithm PrStack can find k SLCA results with the k highest probabilities by scanning the relevant keyword nodes only once. To further improve the efficiency, we propose a second algorithm EagerTopK based on a set of pruning properties which can quickly prune unsatisfied SLCA candidates. Finally, we implement the two algorithms and compare their performance with analysis of extensive experimental results.

An important facility to aid keyword search on XML data is suggesting alternative queries when user queries contain typographical errors. Query suggestion thus can improve users' search experience by avoiding returning empty result or results of poor qualities. In this paper, we study the problem of effectively and efficiently providing quality query suggestions for keyword queries on an XML document. We illustrate certain biases in previous work and propose a principled and general framework, XClean, based on the state-of-the-art language model. Compared with previous methods, XClean can accommodate different error models and XML keyword query semantics without losing rigor. Algorithms have been developed that compute the top-k suggestions efficiently. We performed an extensive experiment study using two large-scale real datasets. The experiment results demonstrate the effectiveness and efficiency of the proposed methods.

Although keyword query enables inexperienced users to easily search XML database with no specific knowledge of complex structured query languages or XML data schemas, the ambiguity of keyword query may result in generating a great number of results that may be classified into different types. For users, each result type implies a possible search intention. To improve the performance of keyword query, it is desirable to efficiently work out the most relevant result type from the data to be retrieved.

Keyword search is integrated in many applications on account of the convenience to convey users' query intention. Recently, answering keyword queries on XML data has drawn the attention of web and database communities, because the success of this research will relieve users from learning complex XML query languages, such as XPath/XQuery, and/or knowing the underlying schema of the queried XML data. As a result, information in XML data can be discovered much easier.