Link Prediction with Hypergraphs via Network Embedding

Abstract

Network embedding is a promising field and is important for various network analysis tasks, such as link prediction, node classification, community detection and others. Most research studies on link prediction focus on simple networks and pay little attention to hypergraphs that provide a natural way to represent complex higher-order relationships. In this paper, we propose a link prediction method with hypergraphs using network embedding (HNE). HNE adapts a traditional network embedding method, Deepwalk, to link prediction in hypergraphs. Firstly, the hypergraph model is constructed based on heterogeneous library loan records of seven universities. With a network embedding method, the low-dimensional vectors are obtained to extract network structure features for the hypergraphs. Then, the link prediction is implemented on the hypergraphs as the classification task with machine learning. The experimental results on seven real networks show our approach has good performance for link prediction in hypergraphs. Our method will be helpful for human behavior dynamics.