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

This paper addresses node classification in hypergraphs (a type of graph where edges can connect more than two nodes) by proposing a method called LHCCA that learns from labeled data in a source hypergraph to help classify unlabeled nodes in a target hypergraph. The method improves on existing approaches by considering both local relationships (direct connections) and high-order relationships (connections at greater distances), combining these through an attention mechanism (a technique that learns which parts of the input to focus on), and using adversarial domain adaptation (a training strategy to make learned features work across different hypergraphs) and contrastive learning (a method that learns by comparing similar and dissimilar examples).