Hypergraph representation learning based on fuzzy attention network

Since different nodes in hypergraph have different contributions to hyperedge learning, it is a hot topic in the field of graph representation learning to introduce attention mechanism into hypergraph neural network to represent the importance of different nodes or hyperedges. However, the existing hypergraph attention network methods are mostly based on the assumption that node attributes and topology are perfectly given. In practice, node links and attributes are usually fuzzy concepts, which will lead to the uncertainty of feature learning and then affect the effectiveness of node representation. Based on this, inspired by the attention mechanism and fuzzy logic, this paper proposes a new fuzzy attention hypergraph neural network (HFATN), which is used to quantify the contribution ability of different vertices and hyperedges to better learn the vector representation of nodes in the hypergraph. HFATN consists of two modules: fuzzy attention vertex convolution and fuzzy attention hyperedge convolution. In the process of node convolution and hyperedge convolution, the attention mechanism based on hyperedge and node uncertainty characteristics is introduced respectively. By fuzzing the node set and hyperedge set on the hypergraph, the membership degree of nodes and hyperedges is calculated, which is used to extract effective features to deal with the uncertainty in the hypergraph data. Finally, we conduct experiments on three benchmark datasets for hypergraph node classification. The results show that compared with the latest TDHGNN model, the classification accuracy of FHATN on the three datasets is improved by 2.28 %, 8.99 % and 1.85 % respectively.