Link Prediction Method Based on Hypergraph Neural Network

With the rapid development of information technology, link prediction has been widely applied in various fields. Current link prediction methods are based on subgraph extraction. Models based on Line Graph Transformation (LGT) and Graph Convolutional Network (GCN) achieve excellent results in link prediction. However, two problems remain: 1) the high time complexity of the LGT and the large size of the line graph hinder its wide-spread application; 2) GCN ignores the high-order relationship and local clustering structure between nodes, thereby affecting prediction accuracy. To solve the above issues, this paper proposes a link prediction method based on Hypergraph Convolutional Network (HGCN), called HGLP. This method replaces LGT with Dual Hypergraph Transformation (DHT) to improve system efficiency without losing structural information and applies HGCN to learn the higher-order features of the hypernodes and hyperedges in the hypergraph to obtain higher prediction accuracy. Experimental results show that the proposed method outperforms state-of-the-art link prediction methods on seven real-world datasets from different domains, in terms of Area Under the Curve (AUC) and Average Precision (AP). Furthermore, the proposed method achieves shorter runtimes and less memory usage.