Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs

Abstract

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 novel method, 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, NeurTWs 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 allows for irregularly-sampled temporal nodes to be embedded explicitly over multiple interaction time intervals, enabling the 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 three real-world datasets.

Ming Jin

Lecturer of AI

My research interests include machine learning and artificial intelligence.

Shirui Pan

Professor and ARC Future Fellow

My research interests include data mining, machine learning, and graph analysis.