Learning Temporal Interaction Graph Embedding via Coupled Memory Networks
WWW '20: The Web Conference 2020 Taipei Taiwan April, 2020, pp. 3049-3055, 2020.
Graph embedding has become the research focus in both academic and industrial communities due to its powerful capabilities. The majority of existing work overwhelmingly learn node embeddings in the context of static, plain or attributed, homogeneous graphs. However, many real-world applications frequently involve bipartite graphs with tem...More
Full Text (Upload PDF)
PPT (Upload PPT)