Computer Science > Machine Learning
[Submitted on 18 Apr 2024 (v1), last revised 25 Jul 2024 (this version, v3)]
Title:Node-like as a Whole: Structure-aware Searching and Coarsening for Graph Classification
View PDF HTML (experimental)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 novel multi-view graph representation learning model via structure-aware searching and coarsening (GRLsc) on GT architecture for graph classification. Specifically, we build three unique views, original, coarsening, and conversion, to learn a thorough structural representation. We compress loops and cliques via hierarchical heuristic graph coarsening and restrict them with well-designed constraints, which builds the coarsening view to learn high-level interactions between structures. We also introduce line graphs for edge embeddings and switch to edge-central perspective to construct the conversion view. Experiments on eight real-world datasets demonstrate the improvements of GRLsc over 28 baselines from various architectures.
Submission history
From: Xiaorui Qi [view email][v1] Thu, 18 Apr 2024 03:03:37 UTC (1,471 KB)
[v2] Mon, 24 Jun 2024 08:45:52 UTC (27,839 KB)
[v3] Thu, 25 Jul 2024 07:29:02 UTC (28,798 KB)
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