Local and High-Order Consistency Coding and Adaptation for Cross-Hypergraph Node Classification

Summary

Researchers address node classification in a newly formed hypergraph, where labeled nodes are scarce and costly to obtain. They propose LHCCA (Local and High-order Consistency Coding and Adaptation), which transfers knowledge from a well-labeled source hypergraph by combining local and high-order consistency representations through attention, then aligns source and target features with adversarial domain adaptation and contrastive learning. Experiments on several real-world datasets are reported to show the model's effectiveness.