Social recommendation, which leverages users' social information to predict users' preferences, is a popular branch of recommender systems. Many existing studies have attempted to advance the performance of collaborative filtering methods by leveraging the user-user matrix to enhance user embedding learning with user's social connections. While the existing social recommender systems have demonstrated good performance in various recommendation tasks, the extent of social information usefulness in recommender systems remains unclear. This paper addresses the research gap by designing experiments to answer three research questions: (i) How useful is social information in varying user-item data sparsity? (ii) How much social information do the existing social recommendation models use? (iii) How valuable is social information for cold-start situations? Working towards answering the research questions, we introduce evaluation metrics to estimate the utilization of social information in the existing social recommendation models. We conducted experiments on three publicly available social recommendation datasets, and our results showed that there are diminishing returns when applying social information in recommender systems.

Point cloud-based human action recognition leverages advanced spatio-temporal local encoding strategies to model the motion patterns, and has achieved remarkable performance. However, existing approaches struggle to precisely model the specific spatial scales and temporal spans of various body parts due to rigid spatio-temporal neighborhood limitations. Furthermore, due to the extraction of spatial configurations and temporal dynamics from isolated components, these methods cause distortions of spatio-temporal interactions. To solve the above problems, we propose a Meta-motion Level Refined Spatio-Temporal Point Convolution Network (MRST-PCN). Firstly, we progressively decouple the dynamic structures of different body parts, and present a logarithmic spatio-temporal point convolution strategy to capture meta-motion patterns at varying spatio-temporal granularities. Secondly, we devise the meta-motion differential strategy to derive short-term spatio-temporal displacements in frame neighborhoods, while designing a gated KANsformer to establish long-term spatio-temporal dependencies along the meta-motion flow. Finally, extensive experiments on three public datasets (MSR Action3D, UTD-MHAD, and NTU RGB+D 60) substantiate the superiority of MRST-PCN over state-of-the-art methods.

Gearboxes are widely used in various types of mechanical equipment and have become an essential part of connecting various components in the machine and transmitting power. A fault in the gearbox will cause the entire mechanical equipment to stop working, causing economic losses and safety hazards. Previous fault diagnosis models for gearbox used the vibration signals of the gearbox as input features. They designed a variety of feature extraction modules to learn the information contained in the vibration signals. However, the latent features learned by previous models have poor interpretability and robustness, thus affecting model performance. In addition, the prediction results of previous fault diagnosis models used for gearbox using single-scale features are often less robust. Moreover, the fault diagnosis models used for gearbox are usually end-to-end black box models, which cannot provide interpretive information about the predicted values, and it is difficult to handle the uncertain information in the vibration signals. Therefore, we propose a multi-scale fuzzy variational autoencoder (MFVAE) using a fuzzy neural network for Big Data-based fault diagnosis in gearbox. The Big Data technology can automatically collect vibration signals from sensors and provide high-quality training samples for fault diagnosis models. The MFVAE model first uses two variational autoencoders of different sizes to extract the latent features of the gearbox vibration signal. Subsequently, the MFVAE model fuses low-level latent features and high-level latent features in proportion to form the final head features, which are used to diagnose gearbox faults. Finally, the MFVAE model inputs the head features into the prediction layer to diagnose the fault of the gearbox. The prediction layer comprises a fully connected network and a fuzzy neural network. Experimental results on the real gearbox dataset verify the outstanding performance of the MFVAE model.

In this article, we investigate the problem of unmanned aerial vehicles (UAVs) formation tracking in the presence of multiple cyber-threats. In this scenario, UAVs have private, potentially conflicting objectives, and their communication networks and local feedback mechanisms are vulnerable to sabotage and eavesdropping by malicious attackers. To address this real-world challenge, we propose a control scheme based on a twin-network structure. Specifically, we construct a virtual twin layer interconnected with the physical layer to design a resilient estimator that fortifies information exchange among UAVs under threats. In addition, by coupling the states from the twin layer and time-varying signals as masks, we achieve privacy protection for critical information. Furthermore, leveraging reliable data provided by the resilient estimator, we design a cooperative controller based on gradient-optimization to update the UAVs' positions. Using Lyapunov theory, we prove that the position of all UAVs converge to a dynamic Nash equilibrium. Finally, we conduct experimental studies to validate the effectiveness of the proposed control scheme.

Accurate vessel trajectory prediction is vital for forecasting navigation trends, reducing potential risks, and ensuring maritime traffic safety. Using automatic identification system (AIS) data to improve the comprehensive performance of vessel future trajectory prediction is an urgent problem in intelligent transportation systems. In this paper, an improved Pelican optimization algorithm (IPOA) optimized bidirectional gated recurrent unit-attention mechanism (BiGRU-AM) vessel trajectory prediction framework based on AIS data is proposed. This method integrates the optimized hyperparameters of IPOA into the BiGRU-AM backbone network. Utilizing the BiGRU-AM network with tuned parameters to achieve bidirectional information enhancement, and establish an efficient IPOA-BiGRU-AM hybrid prediction model. Furthermore, we propose a shared fusion training mechanism (SFTM) for information exchange between modules. A comprehensive evaluation system covering global error, local error and spatial similarity is developed to address the limitations of single indicator evaluation. The trajectory prediction effectiveness of this method is verified on three datasets. Compared with the current methods, this approach improves accuracy by up to 79.30% across all indicators and reduces the average prediction error by 76.31%. In terms of model complexity, our model’s running speed has improved by 49.73% on average, and has a 1.5 times computational advantage compared to baseline models. The experimental results proved the superiority of the proposed method.

This article investigates noncooperative formation control in the presence of malicious agents who disseminate negative information to their neighbors, resulting in misbehavior. A local cost function for agents is introduced to transform the formation control problem into a noncooperative game problem such that the Nash equilibrium solution corresponds to the agents' decisions in the desired formation. The resilient game strategy is proposed, comprising an event-triggered game algorithm and a threshold-based malicious agents detection algorithm, enabling each agent to determine the malice of their neighbors at each trigger moment and selectively interact with them, which guarantees that all normal agents can achieve Nash equilibrium, and the stability is analyzed by Lyapunov theory. Finally, the effectiveness of the algorithms is verified through a multi-unmanned aerial vehicles formation control experiment.

Online rumor detection is crucial for a healthier online environment. Traditional methods mainly rely on content understanding. However, these contents can be easily adjusted to avoid such supervision and are insufficient to improve the detection result. Compared with the content, information propagation patterns are more informative to support further performance promotion. Unfortunately, learning the propagation patterns is difficult, since the retweeting tree is more topologically complicated than linear sequences or binary trees. In light of this, we propose a novel rumor detection framework based on structure-aware retweeting graph neural networks. To capture the propagation patterns, we first design a novel conversion method to transform the complex retweeting tree as more tractable binary tree without losing the reconstruction information. Then, we serialize the retweeting tree as a corpus of meta-tree paths, where each meta-tree can preserve a basic substructure. A deep neural network is then designed to integrate all meta-trees and to generate the global structural embeddings. Furthermore, we propose to integrate content, users, and propagation patterns to enhance more reliable performance. To this end, we propose a novel self-attention-based retweeting neural network to learn individual features from both content and users. We then fuse the node-level features with our global structural embeddings via a mutual attention unit. In this way, we can generate more comprehensive representations for rumor detection. Extensive evaluations on two real-world datasets show remarkable superiorities of our model compared with existing methods.

This article investigates the distributed Nash equilibrium (NE) seeking problem of uncertain multiagent systems in unreliable communication networks. In this problem, the action of each agent is subject to a class of nonlinear systems with uncertain dynamics, and the communication network among agents will be affected by the nonperiodic denial of service (DoS) attacks. Note that, in this insecure network environment, the existence of DoS attacks will directly destroy the connectivity of the network, which leads to performance degradation or even failure of the most existing distributed NE seeking algorithms. To address this problem, we propose a two-stage distributed NE seeking strategy, including the attack-resilient distributed NE estimator and the neuroadaptive tracking controller. The estimator based on the projection subgradient method and the consensus protocol can converge exponentially to virtual NE against DoS attacks. Then, the neuroadaptive tracking controller is designed for uncertain multiagent systems with the output of the estimator as the reference signal such that the actual action of all agents can reach NE. Based on the Lyapunov stability theory and improved average dwell time automaton, the stability of the estimator and the controller is proven, and all signals in the closed-loop system are uniformly bounded. Numerical examples are presented to verify the effectiveness of the proposed strategy.

Clickbait is a sort of news that contains low-quality content but intriguing or overstated titles. Most of the existing studies usually utilize titles to detect clickbait and suffer from poor performance. However, from the perspective of user cognition, the essence of clickbait news is due to the fact that the body contents fall short of the titles’ expectations. So, in addition to the explicit features of titles, many extra informative signals could help to distinguish clickbait. In this article, we proposed a novel Multiview learning approach-based ClickBait Detection (MCBD) model that takes into account useful signals from both titles and body contents. From the view of titles, a multilayer gated convolutional network is employed to learn the local- and long-distance dependence relationships between words. From the view of body contents, we first extract the key topic sentences from the body content and then we developed a novel attention-fused deep relevance matching network (ARMN) to thoroughly mine the similarity between titles and body contents. Combined with the information from the views of titles and body contents, our model can distinguish well-hidden clickbait news. Finally, extensive experiments were conducted on two real-world datasets to demonstrate that the MCBD model outperforms the state-of-the-art models, improving the final performance by 3% in terms of the F1-score. The ablation studies demonstrate that the relevancy between titles and body contents can improve the model’s performance by 1.7% on the F1-score.

With the dramatic growth of various short video platforms, users are more likely to share their social stream online and make their social connections stronger. To better understand their preferences, personality analysis has attracted more attention. Unlike single modal data such as text or images, which is hard to comprehensively uncover one’s personal traits, personality analysis on short video is verified to be much more accurate but also more challenging because of the huge gap between incompatible data modalities. We have noticed that the key problem is how to disentangle the complexity from multimodal data to find their consistency and uniqueness. In this article, we propose a novel video analysis framework for personality detection with visual, acoustic, and textual neural networks. Specifically, to enhance our model’s sensitivity to personality detection, we first propose three deep learning channels to learn modal features. The framework can not only extract each modal feature but also learn time-varying pattern via a temporal alignment network. To identify the consistency and uniqueness across multiple modalities, we creatively propose to maximize the similarity of common information learned by a shared neural network across multiple modalities and extend the distance of exclusive information learned by private networks of different modalities. Extensive experiments on the real-world dataset demonstrate that our model can outperform existing baselines.

Online social networks have greatly facilitated our lives but have also propagated the spreading of rumours. Traditional works mostly find rumors from content, but content can be strategically manipulated to evade such detection, making these methods brittle. To improve the accuracy and robustness of rumor detection, we propose to integrate and exploit the content, propagation structure, and temporal relations because information in the networks always spreads dynamically with significant structures. In this paper, we propose a novel rumor detection framework in online temporal networks via structure learning. Specifically, to exploit the propagation structure, we propose a novel hyperedge walking strategy on a meta-hyperedge graph to learn the representations of sub-structures in the networks. Then a hyperedge expansion method is proposed to generate more global structural features. The expanded hyperedges are more hierarchical, making the learned structural embeddings more expressive. To make full use of content, we design a hypergraph learning model using hyperedge expansion to fuse node content with structural features and generate comprehensive representations for the entire graph. To exploit temporal relations, we design a masked temporal attention unit for learning the evolving patterns of the network. Extensive evaluations with six state-of-the-art baselines on two real-world datasets demonstrate the superiority of our solution.

In the news recommendation, users are overwhelmed by thousands of news daily, which makes the users’ behavior data have high sparsity. Therefore, only considering a single user’s personalized preferences cannot support the news recommendation. How to improve the relatedness of news and users and reduce data sparsity has become a hot issue. Recent studies have attempted to use graph models to enrich the relationship between users and news, but they are still limited to modeling the historical behaviors of a single user. To fill the gap, we integrate user-news relationships and the overall user historical clicked news sequences to construct a global heterogeneous transition graph. And a refinement approach is proposed to recognize the news transition patterns in the graph. Based on the global heterogeneous transition graph, we propose a heterogeneous transition graph attention network to capture the common behavior patterns of most users to enhance the representation of user interest. Fusing the users’ personalized and common interest, we propose the GAINRec model to recommend news effectively. Extensive experiments are conducted on two public news recommendation datasets, and the results show the superiority of the proposed GAINRec model compared with the state-of-the-art news recommendation models. The implementation of our model is available at https://github.com/newsrec/GAINRec .

Agricultural aid is crucial for sub-Saharan Africa to promote agricultural development as countries in this region usually lack sufficient investment to agricultural sectors and farmers. Yet, its effectiveness is still under debated. The conventional empirical literature mostly rely on data of agricultural production and GDP. However, collecting these data is time-consuming. As such, they tend to be outdated, poorly reflecting the effectiveness of agricultural aid timely. This study creatively employs remotely-sensed data to evaluate the effectiveness of agricultural aid in sub-Saharan Africa. We use Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) third generation NDVI (NDVI3g) dataset from 2001 to 2012. The results demonstrate the advantages of remotely sensed data.

Social linking prediction is one of the most fundamental problems in online social networks and has attracted researchers’ persistent attention. Most of the existing works predict unobserved links using graph neural networks (GNNs) to learn node embeddings upon pair-wise relations. Despite promising results given enough observed links, these models are still challenging to achieve heart-stirring performance when observed links are extremely limited. The main reason is that they only focus on the smoothness of node representations on pair-wise relations. Unfortunately, this assumption may fall when the networks do not have enough observed links to support it. To this end, we go beyond pair-wise relations and propose a new and novel framework using hypergraph neural networks with multi-level hyperedge distillation strategies. To break through the limitations of sparsely observed links, we introduce the hypergraph to uncover higher-level relations, which is exceptionally crucial to deduce unobserved links. A hypergraph allows one edge to connect multiple nodes, making it easier to learn better higher-level relations for link prediction. To overcome the restrictions of manually designed hypergraphs, which is constant in most hypergraph researches, we propose a new method to learn high-quality hyperedges using three novel hyperedges distillation strategies automatically. The generated hyperedges are hierarchical and follow the power-law distribution, which can significantly improve the link prediction performance. To predict unobserved links, we present a novel hypergraph neural networks named HNN. HNN takes the multi-level hypergraphs as input and makes the node embeddings smooth on hyperedges instead of pair-wise links only. Extensive evaluations on four real-world datasets demonstrate our model’s superior performance over state-of-the-art baselines, especially when the observed links are extremely reduced.

According to social psychology studies, social networks are strongly subject to group influence; that is, users' behaviors or sentiment are primarily influenced by their group environments. But only a few studies to date have examined group influence in online social networks (OSNs). In this paper, a Factor Graph-based Group Influence model (F2GI) is proposed to depict the influence of perceived groups on a user. First, we analyze individuals' group environments and propose definitions and detecting methods of two types of groups: following groups and interacting groups. Secondly, based on these groups, we quantity the groups' features and propose the F2GI model able to describe group influence. Finally, using the retweet behaviors as the primarily manifestations of group influence, we apply our model to predict individuals' retweet behaviors and to validate how much influence the group puts on individuals. The results show that our group influence model can depict group influence more effectively and predict the actions of users who are affected by perceived groups accurately with social psychology ideas than classical machine learning methods. We believe that, moreover, our model can also be applied to marketing strategies, advertisement strategies and the prediction of public opinion.