TigeCMN: On exploration of temporal interaction graph embedding via Coupled Memory Neural Networks

With the increasing demand of mining rich knowledge in graph structured data, graph embedding has become one of the most popular research topics in both academic and industrial communities due to its powerful capability in learning effective representations. 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 the time, thus putting forward huge challenges in learning effective node representations. Furthermore, most existing graph embedding models try to embed all the information of each node into a single vector representation, which is insufficient to characterize the node's multifaceted properties. 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 the external matrices explicitly and dynamically, which forms deep matrix representations and thus could enhance the expressiveness of the node embeddings. Then, we generate node embedding from two parts: a static embedding that encodes its stationary properties and a dynamic embedding induced from memory matrix that models its temporal interaction patterns. We conduct extensive experiments on various real-world datasets covering the tasks of node classification, recommendation and visualization. The experimental results empirically demonstrate that TigeCMN can achieve significant gains compared with recent state-of-the-art baselines.