Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal Perspective

arXiv:2312.10401v3 Announce Type: replace
Abstract: Graph contrastive learning is a general learning paradigm excelling at capturing invariant information from diverse perturbations in graphs. Recent works focus on exploring the structural rationale from graphs, thereby increasing the discriminability of the invariant information. However, such methods may incur in the mis-learning of graph models towards the interpretability of graphs, and thus the learned noisy and task-agnostic information interferes with the prediction of graphs. To this end, with the purpose of exploring the …

arxiv causal cs.ai cs.lg graph perspective type