Cheeger--Hodge Contrastive Learning for Structurally Robust Graph Representation Learning
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. The proposed signature combines a Cheeger-inspired connectivity signature derived from the algebraic connectivity \(λ_2\) with the low-frequency spectrum of the 1-Hodge Laplacian, thereby capturing both global connectivity an...
| Authors | Mengyang Zhao, Longlong Li, Cunquan Qu |
|---|---|
| Primary category | cs.LG |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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