Temporal motif-based attentional graph convolutional network for dynamic link prediction

Dynamic link prediction is an important component of the dynamic network analysis with many real-world applications. Currently, most advancements focus on analyzing link-defined neighborhoods with graph convolutional networks (GCN), while ignoring the influence of higher-order structural and temporal interacting features on link formation. Therefore, based on recent progress in modeling temporal graphs, we propose a novel temporal motif-based attentional graph convolutional network model (TMAGCN) for dynamic link prediction. As dynamic graphs usually contain periodical patterns, we first propose a temporal motif matrix construction method to capture higher-order structural and temporal features, then introduce a spatial convolution operation following a temporal motif-attention mechanism to encode these features into node embeddings. Furthermore, we design two methods to combine multiple temporal motif-based attentions, a dynamic attention-based method and a reinforcement learning-based method, to allow each individual node to make the most of the relevant motif-based neighborhood to propagate and aggregate information in the graph convolutional layers. Experimental results on various real-world datasets demonstrate that the proposed model is superior to state-of-the-art baselines on the dynamic link prediction task. It also reveals that temporal motif can manifest the essential dynamic mechanism of the network.