<ul><li>Continuous-Time Dynamic Graphs (CTDGs) are important in many real-world applications, motivating the need for Graph Neural Networks (GNNs) tailored to CTDGs.</li><li>This paper provides a comprehensive review of Graph Representation Learning (GRL) on CTDGs, with a focus on Self-Supervised Representation Learning (SSRL).</li><li>The authors introduce a theoretical framework that analyzes the expressivity of CTDG models through an Information-Flow (IF) lens, quantifying their ability to propagate and encode temporal and structural information.</li><li>The paper also categorizes existing CTDG methods and explores SSRL methods tailored to CTDGs, such as predictive and contrastive approaches, which can reduce the reliance on labeled data.</li></ul>

Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review

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