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A faster deep graph clustering network based on dynamic graph weight update mechanism
Deep graph clustering has attracted considerable attention for its potential in handling complex graph-structured data. However, existing approaches...
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Directed dynamic attribute graph anomaly detection based on evolved graph attention for blockchain
Blockchain is gradually becoming an important data storage platform for Internet digital copyright confirmation, electronic deposit, and data...
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Incorporating self-attentions into robust spatial-temporal graph representation learning against dynamic graph perturbations
This paper proposes a Robust Spatial-Temporal Graph Neural Network (RSTGNN), which overcomes the limitations faced by graph-based models against...
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Anomaly graph: leveraging dynamic graph convolutional networks for enhanced video anomaly detection in surveillance and security applications
Video abnormality behavior identification plays a pivotal role in improving the safety and security of surveillance systems by identifying unusual...
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A survey on dynamic graph processing on GPUs: concepts, terminologies and systems
Graphs that are used to model real-world entities with vertices and relationships among entities with edges, have proven to be a powerful tool for...
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Multi-task recommendation based on dynamic knowledge graph
Introducing knowledge graphs into recommender systems effectively solves sparsity and cold start problems. However, existing KG recommendation...
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Graph similarity learning for change-point detection in dynamic networks
Dynamic networks are ubiquitous for modelling sequential graph-structured data, e.g., brain connectivity, population migrations, and social networks....
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Graph-based dynamic attribute clip** for conversational recommendation
Conversational recommender systems (CRS) enable traditional recommender systems to interact with users by asking questions about their preferences...
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Cross-view adaptive graph attention network for dynamic facial expression recognition
Dynamic facial expression recognition is important for human–computer interaction. Learning the temporal dynamic representation of facial expressions...
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Dynamic temporal position observant graph neural network for traffic forecasting
Spatio-temporal forecasting has several applications in neurology, climate, and transportation. One classic example of such a learning assignment is...
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Temporal-order association-based dynamic graph evolution for recommendation
Modeling the interactions between users and items to accurately predict a user preference on items is very crucial for improving the performance of...
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PPDU: dynamic graph publication with local differential privacy
Local differential privacy (LDP) is an emerging privacy-preserving data collection model that requires no trusted third party. Most...
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Dynamic graph neural network-based computational paradigm for video summarization
In recent times, there has been a significant amount of interest in video summary technology. The reason behind video summarizing is to condense the...
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Spatiotemporal synchronous dynamic graph attention network for traffic flow forecasting
Traffic flow forecasting (TFF) is crucial for effective urban planning and traffic management. Most modeling approaches in TFF ignore the dynamic...
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Multi-stage dynamic disinformation detection with graph entropy guidance
Online disinformation has become one of the most severe concerns in today’s world. Recognizing disinformation timely and effectively is very hard,...
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Temporal graph learning for dynamic link prediction with text in online social networks
Link prediction in Online Social Networks—OSNs—has been the focus of numerous studies in the machine learning community. A successful machine...
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DGFormer: a physics-guided station level weather forecasting model with dynamic spatial-temporal graph neural network
In recent years, there has been an increased interest in understanding and predicting the weather using weather station data with Spatial-Temporal...
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Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting
AbstractA smooth traffic flow is very crucial for an intelligent traffic system. Consequently, traffic forecasting is critical in achieving...
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DTM-GCN: A traffic flow prediction model based on dynamic graph convolutional network
A traffic network possesses all the basic characteristics of networks, as well as its own distinct features, which have research significance. In...
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A new method of dynamic network security analysis based on dynamic uncertain causality graph
In the context of cloud computing, network attackers usually exhibit complex, dynamic, and diverse behavior characteristics. Existing research...
