Multi-view Graph Structural Representation Learning via Graph Coarsening

arXiv:2404.11869v1 Announce Type: new
Abstract: Graph Transformers (GTs) have made remarkable achievements in graph-level tasks. However, most existing works regard graph structures as a form of guidance or bias for enhancing node representations, which focuses on node-central perspectives and lacks explicit representations of edges and structures. One natural question is, can we treat graph structures node-like as a whole to learn high-level features? Through experimental analysis, we explore the feasibility of this assumption. Based on our findings, we propose a …

abstract arxiv bias cs.lg cs.si form graph guidance however natural node perspectives question regard representation representation learning tasks transformers type via view