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CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network
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DOI：10.1109/TKDE.2024.3380643.png)
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Hypergraphs can model higher-order relationships among data objects that are found in applications such as social networks and bioinformatics. However, recent studies on hypergraph learning that extend graph convolutional networks to hypergraphs cannot learn effectively from features of unlabeled data. To such learning, we propose a contrastive hypergraph neural network, CHGNN, that exploits self-supervised contrastive learning techniques to learn from labeled and unlabeled data. First, CHGNN includes an adaptive hypergraph view generator that adopts an auto-augmentation strategy and learns a perturbed probability distribution of minimal sufficient views. Second, CHGNN encompasses an improved hypergraph encoder that considers hyperedge homogeneity to fuse information effectively. Third, CHGNN is equipped with a joint loss function that combines a similarity loss for the view generator, a node classification loss, and a hyperedge homogeneity loss to inject supervision signals. It also includes basic and cross-validation contrastive losses, associated with an enhanced contrastive loss training process. Experimental results on nine real datasets offer insight into the effectiveness of CHGNN, showing that it outperforms 19 competitors in terms of classification accuracy consistently.
Keyword:
Task analysis
Training
Generators
Data models
Self-supervised learning
Encoding
Solid modeling
Hypergraphs
contrastive learning
semi-supervised learning
graph neural networks
期刊
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10.4
论文数:
6.9K
被引数:
3.2W
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