Multi-view Graph Structural Representation Learning via Graph Coarsening
Xiaorui Qi Qijie Bai Yanlong Wen Haiwei Zhang Xiaojie Yuan
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 structural representation learning model via graph coarsening
(MSLgo) 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 six
real-world datasets demonstrate the improvements of MSLgo over 14 baselines
from various architectures.