AI RESEARCH
Cheeger--Hodge Contrastive Learning for Structurally Robust Graph Representation Learning
arXiv CS.LG
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ArXi:2604.26301v1 Announce Type: new Graph Contrastive Learning (GCL) has emerged as a prominent framework for unsupervised graph representation learning. However, relying on augmentation design alone to define the invariances learned by GCL can be brittle under structural perturbations. To address this issue, we propose Cheeger--Hodge Contrastive Learning (CHCL), a framework that aligns a perturbation-stable Cheeger--Hodge joint signature across augmented views for robust graph representation learning.