A balanced modularity maximization link prediction model in social networks. Issue 1 (January 2017)

• Record Type: Journal Article
• Authors: Wu, Jiehua; Zhang, Guoji; Ren, Yazhou
• Is Part Of: Information processing & management. Volume 53:Issue 1(2017:Jan.)
• Page Start: 295
• Page End: 307
• Publication Date: 2017-01
• Subjects: Link prediction -- Social network -- Community detection -- Modularity; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2016.10.001
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: We present a novel community-based link prediction method called MMLP. We use modularity maximization process build a bridge a bridge between link prediction and community detection. A trade-off technique is designed to maintain the network in balance. A feature aggregation method is proposed to combine information of different level. Experiments with synthetic and real-world data are presented. Abstract: Link prediction has been becoming an important research topic due to the rapid growth of social networks. Community-based link prediction methods are proposed to incorporate community information in order to achieve accurate prediction. However, the performance of such methods is sensitive to the selection of community detection algorithms, and they also fail to capture the correlation between link formulation and community evolution. In this paper we introduce a balanced Modularity-Maximization Link Prediction (MMLP) model to address this issue. The idea of MMLP is to integrate the formulation of two types of links into a partitioned network generative model. We proposed a probabilistic algorithm to emphasize the role of innerLinks, which correspondingly maximizes the network modularity. Then, a trade-off technique is designed to maintain the network in a stable state of equilibrium. We also present an effective feature aggregation method by exploring two variations of network features. Our proposed method can overcome the limit of several community-based methods and the extensive experimental results on both synthetic and real-world benchmark data demonstrate its effectiveness and robustness. Graphical abstract: