  Although machine learning on hypergraphs has attracted considerable
attention, most of the works have focused on (semi-)supervised learning, which
may cause heavy labeling costs and poor generalization. Recently, contrastive
learning has emerged as a successful unsupervised representation learning
method. Despite the prosperous development of contrastive learning in other
domains, contrastive learning on hypergraphs remains little explored. In this
paper, we propose TriCL (Tri-directional Contrastive Learning), a general
framework for contrastive learning on hypergraphs. Its main idea is
tri-directional contrast, and specifically, it aims to maximize in two
augmented views the agreement (a) between the same node, (b) between the same
group of nodes, and (c) between each group and its members. Together with
simple but surprisingly effective data augmentation and negative sampling
schemes, these three forms of contrast enable TriCL to capture both microscopic
and mesoscopic structural information in node embeddings. Our extensive
experiments using 13 baseline approaches, five datasets, and two tasks
demonstrate the effectiveness of TriCL, and most noticeably, TriCL consistently
outperforms not just unsupervised competitors but also (semi-)supervised
competitors mostly by significant margins for node classification. The code and
datasets are available at https://github.com/wooner49/TriCL.

Machine Learning