Learning fuzzy representations for hypergraph node classification

As an extension of standard graphs, hypergraphs have demonstrated significant advantages in modeling high-order complex relationships compared with standard graphs. Existing literature has witnessed the great success of hypergraph representation learning methods in classifying nodes. However, most of them seek to obtain low-dimensional crisp representations, overlooking the fuzzy and uncertain nature of node attributes. In fact, node attributes such as paper keywords may contain noise or be incomplete, which leads to uncertain semantics. To address this issue, in this paper, we propose learning fuzzy representations for hypergraph node classification. Specifically, we develop a novel method called Hypergraph Collaborative Fuzzy Network (HyperCFN), which studies hypergraph representations with fuzzy logic. Firstly, HyperCFN augments the original hypergraph into two hypergraphs, which are then put into the proposed fuzzy hypergraph encoders. The fuzzy hypergraph encoders consist of hypergraph collaborative networks and fuzzy logic to learn fuzzy representations for every node and hyperedge. Subsequently, the learned representations are enforced node-, hyperedge-, and membership-level contrast. Lastly, to further preserve the hypergraph structure, we develop decoders to reconstruct the augmented hypergraphs. We perform extensive experiments on several datasets, and the promising results demonstrate that the effectiveness of the proposed model and learning fuzzy representations for hypergraphs is valid.