Graph Contrastive Learning with Cohesive Subgraph Awareness
WWW 2024(2024)
摘要
Graph contrastive learning (GCL) has emerged as a state-of-the-art strategy
for learning representations of diverse graphs including social and biomedical
networks. GCL widely uses stochastic graph topology augmentation, such as
uniform node dropping, to generate augmented graphs. However, such stochastic
augmentations may severely damage the intrinsic properties of a graph and
deteriorate the following representation learning process. We argue that
incorporating an awareness of cohesive subgraphs during the graph augmentation
and learning processes has the potential to enhance GCL performance. To this
end, we propose a novel unified framework called CTAug, to seamlessly integrate
cohesion awareness into various existing GCL mechanisms. In particular, CTAug
comprises two specialized modules: topology augmentation enhancement and graph
learning enhancement. The former module generates augmented graphs that
carefully preserve cohesion properties, while the latter module bolsters the
graph encoder's ability to discern subgraph patterns. Theoretical analysis
shows that CTAug can strictly improve existing GCL mechanisms. Empirical
experiments verify that CTAug can achieve state-of-the-art performance for
graph representation learning, especially for graphs with high degrees. The
code is available at https://doi.org/10.5281/zenodo.10594093, or
https://github.com/wuyucheng2002/CTAug.
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