Learning Temporal Interaction Graph Embedding via Coupled Memory Networks

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 temporal and attributed interaction edges, named temporal interaction graphs. The temporal interactions usually imply different facets of interest and might even evolve over time, thus putting forward huge challenges in learning effective node representations. In this paper, we propose a novel framework named TigeCMN to learn node representations from a sequence of temporal interactions. Specifically, we devise two coupled memory networks to store and update node embeddings in external matrices explicitly and dynamically, which forms deep matrix representations and could enhance the expressiveness of the node embeddings. We conduct experiments on two real-world datasets and the experimental results empirically demonstrate that TigeCMN can outperform the state-of-the-arts with different gains.