Closer through commonality: Enhancing hypergraph contrastive learning with shared groups

TL;DR

This paper introduces HyFi, a hypergraph contrastive learning method that leverages high-dimensional hypergraph information and a novel positive sample relationship to improve embedding quality and outperform existing methods.

Contribution

HyFi is a novel hypergraph contrastive learning approach that exploits high-order correlations without corrupting topology, introducing weak positives for fine-grained learning.

Findings

HyFi outperforms baselines in node classification accuracy.

The method is efficient in training speed and GPU memory usage.

It effectively exploits high-dimensional hypergraph information.

Abstract

Hypergraphs provide a superior modeling framework for representing complex multidimensional relationships in the context of real-world interactions that often occur in groups, overcoming the limitations of traditional homogeneous graphs. However, there have been few studies on hypergraphbased contrastive learning, and existing graph-based contrastive learning methods have not been able to fully exploit the highorder correlation information in hypergraphs. Here, we propose a Hypergraph Fine-grained contrastive learning (HyFi) method designed to exploit the complex high-dimensional information inherent in hypergraphs. While avoiding traditional graph augmentation methods that corrupt the hypergraph topology, the proposed method provides a simple and efficient learning augmentation function by adding noise to node features. Furthermore, we expands beyond the traditional dichotomous relationship between positive and negative samples in contrastive learning by introducing a new relationship of weak positives. It demonstrates the importance of fine-graining positive samples in contrastive learning. Therefore, HyFi is able to produce highquality embeddings, and outperforms both supervised and unsupervised baselines in average rank on node classification across 10 datasets. Our approach effectively exploits high-dimensional hypergraph information, shows significant improvement over existing graph-based contrastive learning methods, and is efficient in terms of training speed and GPU memory cost. The source code is available at https://github.com/Noverse0/HyFi.git.