文件名称:
Streaming Graph Neural Networks.pdf
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上传时间: 2020-03-29
详细说明：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 Dynamic Graph Neural Network 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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