Graph neural networks (GNNs) have shown great promise in rumor detection by leveraging user interactions and propagation structures. However, existing GNN-based methods primarily aggregate low-frequency signals, leading to oversmoothing and the loss of distinctive features in user feedback. Additionally, the data imbalance and sparsity in social media platforms hinder the training of robust detection models. To address these challenges, this article proposes the signed graph transformer network (SGTN) and supervised multiprototype contrastive learning (SMPCL) framework. SGTN adaptsively captures low-frequency similarities and high-frequency differences in user comments, effectively enhancing the representation of relationships in rumor propagation. SMPCL introduces learnable prototypes for each class, mitigating the effects of data imbalance and enabling more effective contrastive learning in small batches. Extensive experiments on real-world datasets, including Twitter15, Twitter16, and PHEME, demonstrate the superior performance of the proposed framework. SGTN and SMPCL achieve significant improvements, with the highest accuracy of 89.7% on Twitter15, 91.3% on Twitter16, and 84.3% on PHEME. Compared with state-of-the-art models, SMPCL achieves up to 3.5% F1-score gains in rumor classification tasks, showcasing its robustness and effectiveness in addressing oversmoothing and data imbalance challenges.

Early detection of fake news is crucial to mitigate its negative impact. Current research in fake news detection often utilizes the difference between real and fake news regarding the support degree from reliable sources. However, it has overlooked their different semantic outlier degrees among unreliable source information during the same period. Since fake news often serves idea propaganda, unreliable sources usually publish a lot of information with the same propaganda idea during the same period, making it less likely to be a semantic outlier. To leverage this difference, we propose the Reliable-Unreliable Source Reference (RUSR) Fake News Early Detection Method. RUSR introduces the publication background for detected news, which consists of related news with common main objects of description and slightly earlier publication from both reliable and unreliable sources. Furthermore, we develop a strongly preference-driven support degree evaluation model and a two-hop semantic outlier degree evaluation model, which respectively mitigate the interference of news with weak validation effectiveness and the tightness degree of semantic cluster. The designed redistribution module and expanding range relative time encoding are adopted by both models, respectively optimizing early checkpoint of training and expressing the relevance of news implied by their release time gap. Finally, we present a multi-model mutual benefit and collaboration framework that enables the multi-model mutual benefit of generalization in training and multi-perspective prediction of news authenticity in inference. Experiments on our newly constructed dataset demonstrate the superiority of RUSR.

Fully inductive knowledge graph completion (KGC) aims to predict triplets involving both unseen entities and relations. Recent several approaches transform paths between entities into descriptions and modeling semantic correlations between paths using pre-trained language models (PLMs), have emerged as a promising solution for fully inductive reasoning . However, these methods often adopt a simplistic concatenation strategy for path-to-sentence transformation, which impedes PLMs’ ability to capture subtle nuances in context, resulting in sub-optimal path context embeddings. Furthermore, they ignore the high-order semantics underlying the complete context, which can provide richer information for inductive reasoning . To address these issues, we propose a Multi-Granularity Contextual Semantic (MGCS) modeling framework, utilizing a Path Modeling Network (PMN) and a Subgraph Modeling Network (SMN) to extract two granularity levels of contextual semantics from single paths and complete subgraphs, for fully inductive KGC. The PMN extracts paths between head and tail entities and employs reasoning patterns from similar cases to filter out unreliable paths. Then two innovative path conversion strategies are designed to significantly enhance the pre-trained language model’s understanding of specific path contexts. The SMN employs a neighbor interactive graph neural network to extract high-order semantics from the complete subgraph context with a concept-enhanced relation encoding, and optimizes it through a contrastive learning method. Finally, the confidence of the triples is evaluated from the perspective of global complete context by comparing the semantics between the subgraphs surrounding the target triplet and the subgraphs surrounding similar cases. Experimental results on benchmark datasets demonstrate the effectiveness of MGCS.

Existing community detection methods of heterogeneous graphs focus on homophilous heterogeneous graphs, which generally use GNNs and HGNNs. In heterogeneous graphs with heterophily, the two nodes connected by metapaths often have different labels, and existing methods result in representation collapse among nodes from different communities. To address the challenges posed by structural heterophily and to enhance the strength of homophily, an unsupervised community detection method with multilevel structure fusion oriented by homophily, called SFCDH, is proposed. This method demonstrates a strong generalization capabilities across various homophilous and heterophilous heterogeneous graphs. Specifically, SFCDH consists of two modules named hierarchical structure fusion module based on homophily ratio sampling and the unsupervised graph representation learning module based on generative and contrastive learning . The first module constructs a fine-grained high-order fusion graph based on the network homophily and divides the community into multi-granularity via a hierarchical abstraction tree to improve the generalization ability of the model to different metapath subgraphs. Subsequently, the second module uses contrastive loss based on community prototypes to guide the graph in generating dynamic masks and enhances the ability of SFCDH to learn attributes and complex semantics by using graph autoencoder . Ultimately, node embeddings at the metapath level and the tree level are combined to achieve multilevel structural fusion for unsupervised community detection. Extensive experiments on six publicly available datasets demonstrate the superiority of SFCDH over state-of-the-art baselines. Notably, in the heterophilous heterogeneous graph IMDB, SFCDH outperforms the second-place model by 11% in accuracy, which further validating its effectiveness.

In the realm of scheduling problems, metaheuristics have been widely embraced as superior solutions, appreciated for their ability to generate resolutions for non-deterministic polynomial-time hard (NP-hard) problems swiftly. This paper presents a novel parallel evolutionary algorithm (PEA), which marries metaheuristics and parallel computing to amplify computer performance utilization. Four operators and a restart strategy are incorporated into the proposed PEA to bolster both its global and local search capabilities. An accelerated calculation method for two operators is proposed. The algorithm also features an adaptive method that generates sub-threads and parameters based on computer performance, along with rotation for evaluating solutions. A random search sub-thread is established to update the solution. The algorithm is tested on the workshop automated guided vehicle (AGV) scheduling problem and compared against other optimization algorithms to ascertain its efficacy. The test results overwhelmingly highlight the superior performance of the proposed algorithm. Note to Practitioners—The paper introduces a novel parallel evolutionary algorithm (PEA) for scheduling problems, which combines metaheuristics and parallel computing to enhance computer performance utilization. The algorithm incorporates four operators and a restart strategy, along with an accelerated calculation method for two operators. It also includes an adaptive method to generate sub-threads and parameters based on computer performance, as well as rotation for evaluating solutions. A random search sub-thread is established to update the solution. The proposed algorithm is tested on the workshop automated guided vehicle (AGV) scheduling problem, producing superior results compared to other optimization algorithms. Its ability to swiftly generate resolutions for NP-hard problems can greatly benefit industries that rely on efficient scheduling, such as logistics and manufacturing. However, it is important to note that the algorithm has some limitations. Further research is needed to explore its application in different domains and evaluate its performance in more complex scheduling scenarios. Additionally, the algorithm’s scalability and adaptability need to be thoroughly examined to ensure its practicality in real-world settings.

Production processes are inherently complex, often involving multiple production phases from raw materials to finished products, making joint scheduling problems a focal point of research. This article addresses the dynamic cascaded flowshop joint scheduling problem, which integrates a distributed permutation flowshop in Phase 1 and a hybrid flowshop in Phase 2. The challenge involves both initial scheduling and dynamic response mechanisms for new job insertions. We propose an evolutionary greedy algorithm (EGA) aimed at minimizing total flowtime. The EGA employs a multistart cooperative framework tailored to problem characteristics, alternating between a population-based EGA for Phase 1 and an elitist-based greedy algorithm for Phase 2 to generate a robust and complete schedule. Upon new job insertions, three heuristic-driven response strategies enhance solution stability and adaptability. In addition, phase-specific hybrid local search operators and an adaptive insertion strategy, leveraging knowledge-based problem properties, further improve solution quality and search efficiency. The experimental results indicate that the EGA outperforms five state-of-the-art algorithms, achieving an average improvement of 39% in RPI values across 480 instances. Moreover, the proposed local search mechanisms and dynamic response strategies significantly enhance its performance. Thus, the EGA is well-suited for addressing the studied problem.

Traffic forecasting leverages multivariate time series analysis to predict traffic patterns. Real-world traffic data comprises two distinct types of latent time-series signals:diffuse signals, which refer to time-varying information propagated across the traffic network, andintrinsic signals, which capture unique, location-specific patterns. However, existing approaches often treat traffic signals solely as diffusion outcomes, overlooking the intrinsic characteristics that can significantly influence model performance. To address this issue, we propose the Position-invariant Graph Convolutional Recurrent Network (PGCRN), which decouples diffuse and intrinsic signals for improved traffic forecasting. Instead of relying on a predefined graph, PGCRN learns graph structures from spatio-temporal data through a learnable position-invariant node representation that forms an adaptive adjacency matrix. This is integrated into a Graph Convolutional Recurrent Network (GCRN) encoder–decoder to jointly capture spatial and temporal dependencies. Furthermore, we introduce a contrastive learning framework in which a node’s time-varying and position-invariant representations form positive pairs, while position-invariant representations from different nodes form negative pairs. The model is trained with a triplet loss. Experiments on four benchmark datasets show that PGCRN consistently outperforms strong baselines. Owing to its computational efficiency, PGCRN is also well suited for deployment on resource-constrained edge devices.

MOTIVATION: Spatial transcriptomics allows for the measurement of high-throughput gene expression data while preserving the spatial structure of tissues and histological images. Integrating gene expression, spatial information, and image data to learn discriminative low-dimensional representations is critical for dissecting tissue heterogeneity and analyzing biological functions. However, most existing methods have limitations in effectively utilizing spatial information and high-resolution histological images. We propose a signal-diffusion-based unsupervised contrast learning method (SDUCL) for learning low-dimensional latent embeddings of cells/spots. RESULTS: SDUCL integrates image features, spatial relationships, and gene expression information. We designed a signal diffusion microenvironment discovery algorithm, which effectively captures and integrates interaction information within the cellular microenvironment by simulating the biological signal diffusion process. By maximizing the mutual information between the local representation and the microenvironment representation of cells/spots, SDUCL learns more discriminative representations. SDUCL was employed to analyze spatial transcriptomics datasets from multiple species, encompassing both normal and tumor tissues. SDUCL performed well in downstream tasks such as clustering, visualization, trajectory inference, and differential gene analysis, thereby enhancing our understanding of tissue structure and tumor microenvironments. AVAILABILITY AND IMPLEMENTATION: https://github.com/WeiMin-Li-visual/SDUCL.

Influence maximization (IM) has been extensively researched in the information propagation field and applied in various domains. However, existing studies on the IM have primarily focused on network structure, and lack the in-depth exploration of online network complexities, like personal history or preference. In this paper, a heterogeneous cross-comparison improved Kshell algorithm (HCCKshell) is proposed to solve IM, applying users’ multi-dimensional attributes in the propagation, including social history and topological structure . Specifically, the model learns users’ potential representation of historical content preferences and topological structure based on the Encoder and GCN thoughts, then defines the heterogeneous similarity and the heterogeneous information entropy to measure users’ influence ability and provide reliability assurance on the propagation. To improve the performance, a cross-comparison improved K-shell heuristic algorithm based on the heterogeneous information entropy is proposed to find a valid influential seed set. Furthermore, the experiments on multiple real large-scale datasets and their results indicate that our HCCKshell algorithm is more effective than baseline algorithms on both effect and performance.

Abstract Sequential recommendation is the mainstream approach in the field of click-through-rate (CTR) prediction for modeling users’ behavior. This behavior implies the change of the user’s interest, and the goal of sequential recommendation is to capture this dynamic change. However, existing studies have focused on designing complex dedicated networks to capture user interests from user behavior sequences, while neglecting the use of auxiliary information. Recently, knowledge graph (KG) has gradually attracted the attention of researchers as a structured auxiliary information. Items and their attributes in the recommendation, can be mapped to knowledge triples in the KG. Therefore, the introduction of KG to recommendation can help us obtain more expressive item representations. Since KG can be considered a special type of graph, it is possible to use the graph neural network (GNN) to propagate the rich information contained in the KG into the item representation. Based on this idea, this paper proposes a recommendation method that uses KG as auxiliary information. The method first propagates the knowledge information in the KG using GNN to obtain a knowledge-rich item representation. Then the temporal features in the item sequence are extracted using a transformer for CTR prediction, namely the K nowledge G raph-Aware D eep I nterest E xtraction network (KGDIE). To evaluate the performance of this model, we conducted extensive experiments on two real datasets with different scenarios. The results showed that the KGDIE method could outperform several state-of-the-art baselines. The source code of our model is available at https://github.com/gylgyl123/kgdie .

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a positive-stranded single-stranded RNA virus with an envelope frequently altered by unstable genetic material, making it extremely difficult for vaccines, drugs, and diagnostics to work. Understanding SARS-CoV-2 infection mechanisms requires studying gene expression changes. Deep learning methods are often considered for large-scale gene expression profiling data. Data feature-oriented analysis, however, neglects the biological process nature of gene expression, making it difficult to describe gene expression behaviors accurately. In this article, we propose a novel scheme for modeling gene expression during SARS-CoV-2 infection as networks (gene expression modes, GEM), to characterize their expression behaviors. On this basis, we investigated the relationships among GEMs to determine SARS-CoV-2's core radiation mode. Our final experiments identified key COVID-19 genes by gene function enrichment, protein interaction, and module mining. Experimental results show that ATG10, ATG14, MAP1LC3B, OPTN, WDR45, and WIPI1 genes contribute to SARS-CoV-2 virus spread by affecting autophagy.

While online social media brings convenience to people’s communication, it has also caused the widespread spread of rumors and brought great harm. Recent deep-learning approaches attempt to identify rumors by engaging in interactive user feedback. However, the performance of these models suffers from insufficient and noisy labeled data. In this article, we propose a novel rumor detection model called graph contrastive learning with feature augmentation (FAGCL), which injects noise into the feature space and learns contrastively by constructing asymmetric structures. FAGCL takes user preference and news embedding as the initial features of the rumor propagation tree and then adopts a graph attention network to update node representations. To obtain the graph-level representation for rumor classification, FAGCL fuses multiple pooling techniques. Moreover, FAGCL adopts graph contrastive learning as an auxiliary task to constrain the representation consistency. Contrastive learning on noisy data mines the supervision information of the rumor propagation tree itself, making the model more robust and effective. Results on two real-world datasets demonstrate that our proposed FAGCL model achieves significant improvements over the baseline models.

Group behavior modeling is an important research topic in the field of social network analysis . Existing methods regarding this topic can only learn the static group preference, ignoring the multiple characteristics of the group behavior , so they cannot model the group behavior in a complete way. In this paper, we propose a Dynamic Multi-view Group Preference Learning (DMGPL) model for group behavior prediction in social networks. Firstly, an area-aware user dynamic preference extracting module is developed concerning user representation learning , which can integrate the dynamic user behavior preference and the corresponding group structural information into group members’ representations. Then, to simultaneously obtain stability, propensity, potentiality, and heterogeneity of the group behavior, Multi-view Group Preference Aggregation (MGA) is designed for group behavior modeling. In MGA, various aggregation strategies are used to capture different kinds of group behavior properties, which can achieve the effect of highlighting different properties in different scenarios. Moreover, considering the influence of the member’s size on group behavior, we define the group scaling degree and perform group representation scaling to make MGA more flexible. Finally, extensive experiments are performed on two real-world datasets, and the results show that the accuracy of group behavior prediction by DMGPL improved by about 10% compared to the baseline models .

Nowadays, the concept of Internet of Everything (IoE) is becoming a hotly discussed topic, which is playing an increasingly indispensable role in modern intelligent applications. These applications are known for their real-time requirements under limited network and computing resources, thus it becomes a highly demanding task to transform and compute tremendous amount of raw data in a cloud center. The edge–cloud computing infrastructure allows a large amount of data to be processed on nearby edge nodes and then only the extracted and encrypted key features are transmitted to the data center. This offers the potential to achieve an end–edge–cloud-based big data intelligence for IoE in a typical two-stage data processing scheme, while satisfying a data security constraint. In this study, a deep-reinforcement-learning-enhanced two-stage scheduling (DRL-TSS) model is proposed to address the NP-hard problem in terms of operation complexity in end–edge–cloud Internet of Things systems, which is able to allocate computing resources within an edge-enabled infrastructure to ensure computing task to be completed with minimum cost. A presorting scheme based on Johnson’s rule is developed and applied to preprocess the two-stage tasks on multiple executors, and a DRL mechanism is developed to minimize the overall makespan based on a newly designed instant reward that takes into account the maximal utilization of each executor in edge-enabled two-stage scheduling. The performance of our method is evaluated and compared with three existing scheduling techniques, and experimental results demonstrate the ability of our proposed algorithm in achieving better learning efficiency and scheduling performance with a 1.1-approximation to the targeted optimal IoE applications.

Influence maximization is an important technique for its significant value on various social network applications, such as viral marketing, advertisement, and recommendation. Traditional heuristic algorithms for influence maximization suffer from different issues, such as accuracy descent and information loss during network exploration. Besides, recently proposed deep learning-based approaches cannot extract the in-depth structural information of social networks well. In light of these problems, we propose a novel heuristic method called the network dynamic GCN influence maximization algorithm based on the leader fake labeling mechanism. To exploit the in-depth network topology information for the influence maximization task, we design a network dynamic GCN that owns adaptive layer numbers in terms of different network scales to obtain node representations. Then, considering that there are few labels in the network or the labels are irrelevant to the task of influence maximization, we establish a leader fake labeling mechanism to automatically generate node labels that are helpful to seed nodes selecting for model training. Finally, a heuristic method based on the Mahalanobis distance is developed to quickly select influential seed nodes with the learned node representations. Three real-world datasets are used in our experiments, and the experimental results demonstrate that our algorithm has a better performance for seed set identification under the premise of high efficiency compared with some latest heuristic influence maximization algorithms.

Trajectory data contains rich spatial and temporal information. Turning trajectories into graphs and then analyzing them efficiently in an AI-empowered way is a representative branch of trajectory analysis in IoV and ITS environments, which is of great significance. This research attempts to project trajectories onto road networks to predict traffic conditions. Extracting accurate spatio-temporal dependencies is the key to improving the analysis. However, two problems exist in the current study. The first one is the focus on the network structure while ignoring node features, and the second one is that the structure cannot be fully utilized. In addition, the static spatial structure may not accurately reflect the dynamic real spatial dependency. In response to these problems, a novel Spatio-Temporal Graph Convolutional Networks via View Fusion for Trajectory Data Analytics (STFGCN) model is designed. It contains two independent views: the structural view and feature view. The view fusion layer is further designed. It includes an extended graph convolutional module and a causal dilated module. The extended graph convolutional module fully extracts dynamic spatial dependencies, while the causal dilated module captures time tendencies. Stacked view fusion layers and a view fusion module perform fusion operations based on the advantages of the two views, efficiently integrating information from both. Several experiments are performed on two real-world trajectory datasets. The results show that a better prediction performance is obtained, especially on the long-range time prediction task.

An unprecedented prosperity in artificial intelligence promotes the development of Internet of Vehicles (IoV). Assisted by edge computing, vehicles enable to offload data to edge servers in close proximity to users for processing, thus making up for the shortage of local computing resources. However, due to the uneven space-time distribution of traffic flow, edge servers of a certain road segment may be overwhelmed by the surge of service requests. Furthermore, IoV system will incur significant additional energy consumption and time delay because of the absence of a proper computation offloading scheme between edge servers. To cope with above challenges, a computing offloading method for energy and delay trade-offs with traffic flow prediction in edge computing-enabled IoV is proposed. We first design the graph weighted convolution network (GWCN) that can fully excavate the connectivity and distance relation information between road segments to conduct traffic flow prediction. The short-term prediction results are utilized as the basis for adjusting the resource allocation of edge resources in different regions. Then, a computation offloading method driven by deep deterministic policy gradient (DDPG) is leveraged to obtain an optimal computation offloading scheme for edge servers. Finally, extensive comparative experiments demonstrate the low prediction error of GWCN and superior performance of DDPG-driven method in reducing total time delay and energy consumption.

Question answering over knowledge graph (KGQA) aims at answering questions posed over the knowledge graph (KG). Multi-hop KGQA requires multi-hop reasoning on KG to achieve the correct answer. Unfortunately, KGs are usually incomplete with many missing links, which poses additional challenges to KGQA. KG embedding-based KGQA methods have recently been proposed as a way to overcome this limitation. However, existing KG embedding-based KGQA methods fail to take full advantage of semantic correlations between questions and paths. Furthermore, their inference process is not easily explainable. To address these challenges, we propose a novel path-aware multi-hop KGQA model (PA-KGQA), which can fully capture semantic correlations between the paths and the questions in a feature-interactive manner. Specifically, we introduce a case-enhanced path retriever to evaluate the importance of paths between topic entities and candidate answer entities, and then propose an interactive convolutional neural network (ICNN) to model the interactions between paths and questions for mining richer correlation features. Experiments show that PA-KGQA achieves state-of-the-art results on multiple benchmark datasets and is explainable.

Summary Behavior is autonomous, convergent, and uncertain, which brings challenges to the modeling of social network behavior spread. In this article, we propose a behavior spread model based on group cohesion under uncertain environments. First, for behavioral convergence, we define group cohesion to quantify the convergent effects of group. Second, based on the game theory to model the autonomy of behavior, according to the characteristics of the game payoffs changing with time and the depth of spread, and integrating group cohesion, a dynamic game payoffs calculation method is designed. Finally, aiming at the uncertainty of behavior, a group behavior spread model based on random utility theory is established. Experiments on multiple real social network behavior spread datasets demonstrate the effectiveness of the proposed model in modeling and predicting behavior spread processes under uncertain environments.

Summary The spread of rumors has a major negative impact on social stability. Traditional rumor spreading models are mostly based on infectious disease models and do not consider the influence of individual differences and the network structure on rumor spreading. In this paper, we propose a rumor Fick‐spreading model that integrates information decay in social networks. The dissemination of rumors in social networks is random and uncertain and is affected by the dissemination capabilities of individuals and the network environment. The rumor Fick‐transition coefficient and Fick‐transition gradient are defined to determine the influence of the individual transition capacity and the network environment on rumor propagation, respectively. The Fick‐state transition probability is used to describe the probability of change of an individual's state. Moreover, an information decay function is defined to characterize the self‐healing probability of individuals. According to the different roles and reactions of users during rumor dissemination, the user state and the rumor dissemination rules among users are refined, and the influence of the network structure on the rumor dissemination is ascertained. The experimental results demonstrate that the proposed model outperforms other rumor spread models.

The advancement of Internet of Things (IoT) technologies leads to a wide penetration and large-scale deployment of IoT systems across an entire city or even country. While IoT systems are capable of providing intelligent services, the large amount of data collected and processed in IoT systems also raises serious security concerns. Many research efforts have been devoted to design intelligent network intrusion detection system (NIDS) to prevent misuse of IoT data across smart applications. However, existing approaches may suffer from the issue of limited and imbalanced attack data when training the detection model, which make the system vulnerable especially for those unknown type attacks. In this study, a novel hierarchical adversarial attack (HAA) generation method is introduced to realize the level-aware black-box adversarial attack strategy, targeting the graph neural network (GNN)-based intrusion detection in IoT systems with a limited budget. By constructing a shadow GNN model, an intelligent mechanism based on a saliency map technique is designed to generate adversarial examples by effectively identifying and modifying the critical feature elements with minimal perturbations. A hierarchical node selection algorithm based on random walk with restart (RWR) is developed to select a set of more vulnerable nodes with high attack priority, considering their structural features, and overall loss changes within the targeted IoT network. The proposed HAA generation method is evaluated using the open-source data set UNSW-SOSR2019 with three baseline methods. Comparison results demonstrate its ability in degrading the classification precision by more than 30% in the two state-of-the-art GNN models, GCN and JK-Net, respectively, for NIDS in IoT environments.

Most research on influence maximization focuses on the network structure features of the diffusion process but lacks the consideration of multi-dimensional characteristics. This paper proposes the attributed influence maximization based on the crowd emotion, aiming to apply the user’s emotion and group features to study the influence of multi-dimensional characteristics on information propagation. To measure the interaction effects of individual emotions, we define the user emotion power and the cluster credibility, and propose a potential influence user discovery algorithm based on the emotion aggregation mechanism to locate seed candidate sets. A two-factor information propagation model is then introduced, which considers the complexity of real networks. Experiments on real-world datasets demonstrate the effectiveness of the proposed algorithm. The results outperform the heuristic methods and are almost consistent with the greedy methods yet with improved time performance.

Multi-view learning models the relationships between various observations, and is adept to explore the underlying information of data from multiple perspectives. Since well representation is vital for self-expressive subspace clustering, we propose a method called Multi-View Subspace Clustering with Consistent and view-Specific Latent Factors and Coefficient Matrices (MVSC-CSLFCM) that explores the consensus and complementary information of multiple views, and we also impose suitable constraints on coefficient matrices corresponding to the obtained view-specific and consistent representations, respectively. Finally, an effective optimization algorithm based on augmented lagrangian multiplier is introduced to optimize our proposed MVSC-CSLFCM. Comprehensive experiments on four real-world data sets demonstrate the superiority of our proposed method by comparing with a series of state-of-art subspace algorithms.

Heterogeneous graph containing different types of nodes or links is one of graph types, which is most relevant to actual problems. However, the research for heterogeneous graph has not been studied adequately. In this paper, we propose a new model named Heterogeneous Graph Gated Attention Network (HGGAN) to process heterogeneous graph, including node feature space unification, center-neighbor nodes (C-N) aggregation and metapath-metapath (M-M) aggregation. Especially, we use multihead attention mechanism in C-N aggregation. Owing to the contribution of each attention head is different, so we use a convolutional sub-network to assign a parameter to reflect the contribution of different attention heads. Experimental results on three real-word heterogeneous datasets show that HGGAN achieves state-of-the-art results on node classification task.

The recent increase of asynchronous event sequence data in a diversity of fields, make researchers pay more attention to how to mine knowledge from them. In the initial research phase, researchers tend to make use of basic mathematical-based point process models, such as Poisson process and Hawkes process. And in recent years, recurrent neural network (RNN) based point process models are proposed which have significant model performance improvement, while it is still hard to describe the long-term relation between events. To address this issue, transformer Hawkes process is proposed. However, it is worth noting that transformer with a fixed stack of different layers is failure to implement the parallel processing, recursive learning, and abstracting the local salient properties, while they may be very important. In order to make up for this shortcoming, we present a Universal Transformer Hawkes Process (UTHP), which introduces the recurrent structure in encode process, and introduce convolutional neural network (CNN) in the position-wise-feed-forward neural network. Experiments on several datasets show that the performance of our model is improved compared to the performance of the state-of-the-art.

The influence of each entity in a network is a crucial index of the network information dissemination. Greedy influence maximization algorithms suffer from time efficiency and scalability issues. In contrast, heuristic influence maximization algorithms improve efficiency, but they cannot guarantee accurate results. Considering this, this paper proposes a Gaussian propagation model based on the social networks. Multi-dimensional space modeling is constructed by offset, motif, and degree dimensions for propagation simulation. This space’s circumstances are controlled by some influence diffusion parameters. An influence maximization algorithm is proposed under this model, and this paper uses an improved CELF algorithm to accelerate the influence maximization algorithm. Further, the paper evaluates the effectiveness of the influence maximization algorithm based on the Gaussian propagation model supported by theoretical proofs. Extensive experiments are conducted to compare the effectiveness and efficiency of a series of influence maximization algorithms. The results of the experiments demonstrate that the proposed algorithm shows significant improvement in both effectiveness and efficiency.

Breast tumor classification with multiple medical reports such as B-ultrasound, Mammography (X-ray) and Nuclear Magnetic Resonance Imaging (MRI) is crucial to the intelligent cancer diagnosis system. Unlike the other domain texts, the medical reports have latent hierarchical syntactic structures and have hidden rich semantic information about the entities and relationships, which poses a great challenge of breast cancer classification. In this article, we proposed a Knowledge-powered Deep Breast Tumor Classification model (KDBTC), which takes the semantic information as a kind of prior knowledge and incorporated it into deep neural networks. Specially, our proposed model first uses Hierarchical Attention Bidirectional Recurrent Neural Networks (HA-BiRNNs) to encode the syntax-aware representation of medical reports in a hierarchical way. In the HA-BiRNN, a hierarchical neural network structure, consisting in two encoder layers of BiRNN (Bidirectional Recurrent Neural Networks), mirrors the hierarchical structure of medical reports, and a hierarchical attention mechanism, consisting of two levels attentions, attends to important elements within clinical report with word-level attention and sentence-level attention. Secondly, our model obtains the semantic information relevant to the medical reports from the clinical domain semantic tree, and encodes the semantic representation of medical reports by using Tree Structured Recurrent Neural Network with gated recursive units (Tree-GRUs). Finally, we classify breast tumors by combining both the syntax and semantic representations of medical reports. We evaluate our method on the real-world breast cancer medical reports, and results show that our method achieves higher performance on breast cancer classification.

With the ever-increasing maturity and popularization of the Internet of Things (IoT), tremendous business applications developed by distinct enterprises or organizations have been encapsulated into lightweight web services that can easily be accessed or invoked remotely. However, the big volume of candidate web services places a heavy burden on the users' service selection decision-making process. Under the circumstance, a variety of intelligent recommendation solutions have been developed to reduce the high decision-making cost. Traditional resolutions usually challenge in two aspects. First, the recommendation parameters, i.e., the quality of services (QoS), usually relies on user/service location heavily; therefore, low-quality recommended results may be returned to users if user/service location information is overlooked. Second, historical QoS data often contain partial sensitive information of users; therefore, it becomes a necessity to protect the sensitive QoS data while making accurate recommendation decisions. To tackle the above challenges, we introduce the concepts of user/service location information and locality-sensitive hashing (LSH) in the domain and propose a location-aware recommendation approach with privacy-preservation capability. A wide range of experiments is set up based on the popular WS-DREAM data set, whose results prove the effectiveness and efficiency of our approach.

Recommendation system plays an important role in online web applications. Sequential recommender further models user short-term preference through exploiting information from latest user-item interaction history. Most of the sequential recommendation methods neglect the importance of ever-changing item popularity. We propose the model from the intuition that items with most user interactions may be popular in the past but could go out of fashion in recent days. To this end, this paper proposes a novel sequential recommendation approach dubbed TRec, TRec learns item trend information from implicit user interaction history and incorporates item trend information into next item recommendation tasks. Then a self-attention mechanism is used to learn better node representation. Our model is trained via pairwise rank-based optimization. We conduct extensive experiments with seven baseline methods on four benchmark datasets, The empirical result shows our approach outperforms other state-of-the-art methods while maintains a superiorly low runtime cost. Our study demonstrates the importance of item trend information in recommendation system designs, and our method also possesses great efficiency which enables it to be practical in real-world scenarios.

Emerging cyber technology permeates not only our working environment but also our daily life. We enjoy various cyber-enabled online services, e.g., using smartphones for navigation and friendships via social network services (SNS) and doing business through the cyberspace, where a large amount of human-centric data are produced in both the man–man and man–machine systems. This issue aims to enhance the next generation of human-centered social computing and provide relevant theoretical and algorithmic support for the application of social networks. Accepted articles included in this special issue cover topics on human-centric big data analysis, social influence analysis, data-driven interdisciplinary modeling and analysis, user behavior modeling and analysis, cyber-enabled and artificial intelligent (AI)-enhanced healthcare services, and so on. These contents may draft promising perspectives for future research and demonstrate a variety of issues and solutions related to social computing. With the AI-enhanced technical analysis, these human-centric data can be effectively used to better serve humanity.