Self-supervised Learning and Graph Classification under Heterophily
Yilin Ding Zhen Liu Hao Hao
Self-supervised learning has shown its promising capability in graph
representation learning in recent work. Most existing pre-training strategies
usually choose the popular Graph neural networks (GNNs), which can be seen as a
special form of low-pass filter, fail to effectively capture heterophily. In
this paper, we first present an experimental investigation exploring the
performance of low-pass and high-pass filters in heterophily graph
classification, where the results clearly show that high-frequency signal is
important for learning heterophily graph representation. On the other hand, it
is still unclear how to effectively capture the structural pattern of graphs
and how to measure the capability of the self-supervised pre-training strategy
in capturing graph structure. To address the problem, we first design a
quantitative metric to Measure Graph Structure (MGS), which analyzes
correlation between structural similarity and embedding similarity of graph
pairs. Then, to enhance the graph structural information captured by
self-supervised learning, we propose a novel self-supervised strategy for
Pre-training GNNs based on the Metric (PGM). Extensive experiments validate our
pre-training strategy achieves state-of-the-art performance for molecular
property prediction and protein function prediction. In addition, we find
choosing the suitable filter sometimes may be better than designing good
pre-training strategies for heterophily graph classification.