Instant Graph Neural Networks for Dynamic Graphs

Abstract

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 ..