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Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs
Ming Jin · Yuan-Fang Li · Shirui Pan

Thu Dec 01 09:00 AM -- 11:00 AM (PST) @ Hall J #201

Continuous-time dynamic graphs naturally abstract many real-world systems, such as social and transactional networks. While the research on continuous-time dynamic graph representation learning has made significant advances recently, neither graph topological properties nor temporal dependencies have been well-considered and explicitly modeled in capturing dynamic patterns. In this paper, we introduce a new approach, Neural Temporal Walks (NeurTWs), for representation learning on continuous-time dynamic graphs. By considering not only time constraints but also structural and tree traversal properties, our method conducts spatiotemporal-biased random walks to retrieve a set of representative motifs, enabling temporal nodes to be characterized effectively. With a component based on neural ordinary differential equations, the extracted motifs allow for irregularly-sampled temporal nodes to be embedded explicitly over multiple different interaction time intervals, enabling the effective capture of the underlying spatiotemporal dynamics. To enrich supervision signals, we further design a harder contrastive pretext task for model optimization. Our method demonstrates overwhelming superiority under both transductive and inductive settings on six real-world datasets.

Author Information

Ming Jin (Monash University)
Yuan-Fang Li (Monash University)
Shirui Pan (Griffith University)

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