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

Node classification is a fundamental task in hypergraph learning. Existing methods generally assume that there are a few labeled nodes given in advance. However, in a newly formed hypergraph, collecting label information is challenging and costly in practice. Besides, current approaches mainly exploit the local consistency relationship, i.e., direct neighborhood information, while ignoring the high-order consistency relationship, i.e., high-order proximity information, limiting the discrimination of the latent representations. To address these issues, we propose leveraging knowledge from an auxiliary well-labeled hypergraph (source hypergraph) to assist the learning tasks in the target hypergraph, thus studying the cross-hypergraph node classification problem. Specifically, we propose a model, namely Local and High-order Consistency Coding and Adaptation (LHCCA), which learns both discriminative and transferable node representations. On the one hand, for each hypergraph, by exploiting the local and high-order consistency relationships, LHCCA obtains two kinds of representations, which are then coded by an attention mechanism to achieve a unified representation. On the other hand, the coded source and target node representations are enforced adversarial domain adaptation and contrastive learning to discover transferable features for adaptation. Furthermore, we derive theoretical analyses to establish desirable properties of the proposed model. Extensive experiments on several real-world datasets are conducted, and the promising results demonstrate the effectiveness of the proposed model.