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Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
Tianxin Wei · Yuning You · Tianlong Chen · Yang Shen · Jingrui He · Zhangyang Wang

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #631

This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL). We focus on the following question: How to construct contrastive views for hypergraphs via augmentations? We provide the solutions in two folds. First, guided by domain knowledge, we fabricate two schemes to augment hyperedges with higher-order relations encoded, and adopt three vertex augmentation strategies from graph-structured data. Second, in search of more effective views in a data-driven manner, we for the first time propose a hypergraph generative model to generate augmented views, and then an end-to-end differentiable pipeline to jointly learn hypergraph augmentations and model parameters. Our technical innovations are reflected in designing both fabricated and generative augmentations of hypergraphs. The experimental findings include: (i) Among fabricated augmentations in HyperGCL, augmenting hyperedges provides the most numerical gains, implying that higher-order information in structures is usually more downstream-relevant; (ii) Generative augmentations do better in preserving higher-order information to further benefit generalizability; (iii) HyperGCL also boosts robustness and fairness in hypergraph representation learning. Codes are released at https://github.com/weitianxin/HyperGCL.

Author Information

Tianxin Wei (University of Illinois, Urbana-Champaign)
Yuning You (Texas A&M University)
Tianlong Chen (Unversity of Texas at Austin)
Yang Shen (Texas A&M University)
Jingrui He (University of Illinois at Urbana-Champaign)
Zhangyang Wang (University of Texas at Austin)

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