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Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining by Zheng, Yanping (author);Wang, Hanzhi (author);Wei, Zhewei (author);Liu, Jiajun (author);Wang, Sibo (author) is a scholarly article available to read on EtoBox.

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Graph Neural Networks (GNNs) have been widely used for modeling graph-structured data. Recent breakthroughs have been made in improving the scalability of GNNs to work on graphs with millions of nodes. However, how to instantly represent continuous changes of large-scale dynamic graphs with GNNs is still an open problem. Existing dynamic GNNs focus on modeling the periodic evolution of graphs, often on a snapshot basis. Such methods suffer from two drawbacks: first, there is a substantial delay for the changes in the graph to be reflected in the graph representations, resulting in losses on the model's accuracy; second, repeatedly calculating the representation matrix on the entire graph in each snapshot is predominantly time-consuming and severely limits the scalability. In this paper, we propose Instant Graph Neural Network (InstantGNN), an incremental computation approach for the graph representation matrix of dynamic graphs. Set to work with dynamic graphs with the edge-arrival model, our method avoids time-consuming, repetitive computations and allows instant updates on the representation and instant predictions. Graphs with dynamic structures and dynamic attributes are both s

Author

Zheng, Yanping (author);Wang, Hanzhi (author);Wei, Zhewei (author);Liu, Jiajun (author);Wang, Sibo (author)

Publisher

ACM

Published

2022

Language

EN