DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks

著者: Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, Hao Yang - ACM International Conference on Web Search and Data Mining, WSDM 2020 被引用: 580

Learning node representations in graphs is important for many applications such as link prediction, node classification, and community detection. Existing graph representation learning methods primarily target static graphs while many real-world graphs evolve over time. Complex time-varying graph structures make it challenging to learn informative node representations over time.