Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast

• URL: http://arxiv.org/abs/2112.08733v1
• Date: Thu, 16 Dec 2021 09:35:34 GMT
• Title: Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast
• Authors: Linpu Jiang, Ke-Jia Chen, Jingqiang Chen
• Abstract summary: This paper proposes a self-supervised dynamic graph representation learning framework (DySubC) DySubC defines a temporal subgraph contrastive learning task to simultaneously learn the structural and evolutional features of a dynamic graph. Experiments on five real-world datasets demonstrate that DySubC performs better than the related baselines.
• Score: 0.8379286663107846
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised learning on graphs has recently drawn a lot of attention due to its independence from labels and its robustness in representation. Current studies on this topic mainly use static information such as graph structures but cannot well capture dynamic information such as timestamps of edges. Realistic graphs are often dynamic, which means the interaction between nodes occurs at a specific time. This paper proposes a self-supervised dynamic graph representation learning framework (DySubC), which defines a temporal subgraph contrastive learning task to simultaneously learn the structural and evolutional features of a dynamic graph. Specifically, a novel temporal subgraph sampling strategy is firstly proposed, which takes each node of the dynamic graph as the central node and uses both neighborhood structures and edge timestamps to sample the corresponding temporal subgraph. The subgraph representation function is then designed according to the influence of neighborhood nodes on the central node after encoding the nodes in each subgraph. Finally, the structural and temporal contrastive loss are defined to maximize the mutual information between node representation and temporal subgraph representation. Experiments on five real-world datasets demonstrate that (1) DySubC performs better than the related baselines including two graph contrastive learning models and four dynamic graph representation learning models in the downstream link prediction task, and (2) the use of temporal information can not only sample more effective subgraphs, but also learn better representation by temporal contrastive loss.

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