I’m Me, We’re Us, and I’m Us: Tri-directional Contrastive Learning on Hypergraphs

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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 node- and group-level structural information in node embeddings. Our extensive experiments using 14 baseline approaches, 10 datasets, and two tasks demonstrate the effectiveness of TriCL, and most noticeably, TriCL almost 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.
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Issue Date
2023-02-09
Language
English
Citation

37th AAAI Conference on Artificial Intelligence, AAAI 2023, pp.8456 - 8464

DOI
10.1609/aaai.v37i7.26019
URI
http://hdl.handle.net/10203/316064
Appears in Collection
AI-Conference Papers(학술대회논문)
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