Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative

• Published in: Advances in neural information processing systems Vol. 35; p. 1909
• Main Authors: Wei, Tianxin, You, Yuning, Chen, Tianlong, Shen, Yang, He, Jingrui, Wang, Zhangyang
• Format: Journal Article
• Language: English
• Published: United States 01.12.2022
• ISSN: 1049-5258

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 ). We focus on the following question: We provide the solutions in two folds. First, guided by domain knowledge, we 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 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.