Streaming Graph Neural Networks

Abstract

Graphs are essential representations of many real-world data such as social networks. Recent years have witnessed the increasing efforts made to extend the neural network models to graph-structured data. These methods, which are usually known as the graph neural networks, have been applied to advance many graphs related tasks such as reasoning dynamics of the physical system, graph classification, and node classification. Most of the existing graph neural network models have been designed for static graphs, while many real-world graphs are inherently dynamic. For example, social networks are naturally evolving as new users joining and new relations being created. Current graph neural network models cannot utilize the dynamic information in dynamic graphs. However, the dynamic information has been proven to enhance the performance of many graph analytic tasks such as community detection and link prediction. Hence, it is necessary to design dedicated graph neural networks for dynamic graphs. In this paper, we propose DGNN, a new {\bf D}ynamic {\bf G}raph {\bf N}eural {\bf N}etwork model, which can model the dynamic information as the graph evolving. In particular, the proposed framework can keep updating node information by capturing the sequential information of edges (interactions), the time intervals between edges and information propagation coherently. Experimental results on various dynamic graphs demonstrate the effectiveness of the proposed framework.

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Cited by in corpus (12)

• Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
• Deep Learning on Graphs: A Survey
• Graph Neural Networks: Taxonomy, Advances and Trends
• Temporal Graph Networks for Deep Learning on Dynamic Graphs
• Time2Vec: Learning a Vector Representation of Time
• A Survey on Dynamic Network Embedding
• Graph Neural Networks for User Identity Linkage
• Graph Convolutional Networks with EigenPooling
• Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook
• Deep learning reveals hidden interactions in complex systems
• Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook
• Multi-Relation Aware Temporal Interaction Network Embedding