2021
DOI: 10.48550/arxiv.2112.08733
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Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast

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 def… Show more

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