Link prediction in multi-relational networks based on relational similarity

Abstract

Many real-world networks contain multiple types of interactions and relations. Link prediction in such multi-relational networks has become an important area in network analysis. For link prediction in multi-relational networks, we should consider the similarity and influence between different types of relations. In this paper, we propose a link prediction algorithm in multi-relational networks based on relational similarity. In the algorithm, a belief propagation method is presented to calculate the belief of each node and to construct the belief vector for each type of link. We use the similarity between belief vectors to measure the influence between different types of relations. Based on the influence between different relations, we present a nonnegative matrix factorization -based method for link prediction in multi-relational networks. The convergence and correctness of the presented method are proved. Our experimental results show that our method can achieve higher-quality prediction results than other similar algorithms.