Title: Simultaneous community discovery and user interests extraction in social network based on probabilistic model

Authors: Juan Bi; Zhiguang Qin; Hu Xiong; Jia Huang

Addresses: School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China ' School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China ' School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China ' China Science Publishing & Media Ltd. (Science Press), No. 1 6 Sanse Road, Chengdu, 610061, China

Abstract: This article addresses the problem of discovering latent communities and topics simultaneously in social network. With the advent of online social networking, the automatic discovering communities is vital for understanding the cooperation and interaction patterns of users in these social networks. In this paper, we propose probabilistic generative models to detect latent communities by incorporating both the information of relationships and the textural content. Different from previous work, topics and user community memberships cannot be generated independently, but have a greater degree of correspondence between them. We assume that community membership is dependent on the user and a subset of topics which the user is really interested in. Furthermore, the heterogeneous relationship strengths were used to improve community discovery. These models treat community and topic as different latent variables but interdependent with each other and mutually reinforcing. Experiments on real-world dataset have shown that our models have the capability to detect well-connected and topically meaningful communities.

Keywords: community discovery; latent Dirichlet allocation; LDA; probabilistic generative modelling; social networks; user interests extraction; probabilistic modelling; latent communities.

DOI: 10.1504/IJIIDS.2014.066637

International Journal of Intelligent Information and Database Systems, 2014 Vol.8 No.3, pp.260 - 279

Received: 09 Aug 2013
Accepted: 06 Nov 2013
Published online: 11 Jan 2015 *

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