PILHNB: Popularity, interests, location used hidden Naive Bayesian-based model for link prediction in dynamic social networks

Abstract

Link prediction aims to predict the missing interactions in evolving networks that may appear in the future. It has practical importance in various real-world applications, ranging from friendship recommendation, knowledge graph completion, target advertising, and protein–protein interaction prediction. Most of the recent efforts focus on the structure of the network while ignoring many other essential factors. In this paper, we present a modified Latent Dirichlet Allocation (LDA), and Hidden Naive Bayesian (HNB) based link prediction technique named PILHNB model for link prediction in dynamic social networks by considering behavioral controlling elements like relationship network structure, nodes’ attributes, location-based information of nodes, nodes’ popularity, users’ interests, and learning the evolution pattern of these factors in the networks. Experimental results on six real-world networks demonstrate our proposed models’ effectiveness and efficiency compared with existing state-of-the-art link prediction techniques. © 2021 Elsevier B.V.