Self-supervised Dynamic Graph Embedding with evolutionary neighborhood and community

Real-world data and systems are generally evolutionary over time, which can be formalized as dynamic graphs, and Dynamic Graph Embedding (DGE) as a fundamental technique for analyzing dynamic graphs, has shown considerable potential in recent years. However, the majority of existing DGEs focus only on the local neighborhood structure, ignoring the global community structure, which is also an important property of dynamic graphs. Therefore, how to fuse both local and global information to learn more comprehensive graph representations and their variation patterns is a meaningful and promising topic. In this paper, we propose a novel DGE method to explore the graph properties and evolution patterns from Neighborhood and Community views, named DGNC. The neighborhood view learns the node embeddings in line with timestamped interaction events, and models the local variation of graphs via the time-varying attention vector. While the community view encodes the alternative transformation of node features and community features according to community correlation, which aims to extract the global evolution of graphs. To better integrate the two views, cross-supervised contrastive learning is introduced to enable them to collaboratively provide guidance and complementary information to each other, which can significantly improve the quality of graph embedding. We evaluate DGNC through diverse downstream tasks on four real-world dynamic graph datasets, and the experimental results verify the effectiveness and superiority of our proposed method.