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Ego-network probabilistic graphical model for discovering on-line communities

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Abstract

Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (General Program) under Grant No. 61572253, the 13th Five-Year Plan Equipment Pre-Research Projects Fund under Grant No. 61402420101HK02001, and the Aviation Science Fund under Grant No. 2016ZC52030.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Yudao St., Nanjing, Jiangsu Province, 210016, China

    Fei Ding & Yi Zhuang

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  1. Fei Ding
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  2. Yi Zhuang
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Correspondence to Yi Zhuang.

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Ding, F., Zhuang, Y. Ego-network probabilistic graphical model for discovering on-line communities. Appl Intell 48, 3038–3052 (2018). https://doi.org/10.1007/s10489-018-1137-y

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