Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment

arXiv:2302.07491v3 Announce Type: replace
Abstract: Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast to static graphs, temporal graphs are typically organized as node interaction sequences over continuous time rather than an adjacency matrix. Most temporal graph learning methods model current interactions by incorporating historical neighborhood. However, such methods only consider first-order temporal information while disregarding crucial high-order structural information, resulting in suboptimal performance. To address this …

abstract alignment arxiv attention continuous contrast cs.ai cs.lg cs.si dynamic generate graph graph-based graph learning graphs information intensity matrix node quality tasks temporal type