Guangdong University of Technology, Guangdong Province, 510520, China

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Abstract—Community detection in social networks has a great uncertainty and it has commercial value for business. Many of these communities are the target markets to business. Research found that some factors played important roles for social community mining, such as user profile, information of out-degree and in-degree. This paper takes full advantage of user information to model Hybrid Bayesian networks which has strong ability to deal with uncertain event. We begin mining data from users’ profile in social networks and process these data into binary logic data and discrete ones. Combining these two kinds of data, we build Hybrid Bayesian networks, then use graph theory to simplify the process of calculation. Finally, we find that this Hybrid Bayesian networks can provide more accurate and intelligent community detection and it can be applied to different target users and target network communities. Then firms can provide better services for target market.

Index Terms—Bayesian Network; Graph theory; Community detection; Social network

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Cite: Hongwei Liu, Li Chen, Hui Zhu, Tao Lu, Fei Liang , "Uncertainty Community Detection in Social Networks," Journal of Software vol. 9, no. 4, pp. 1045-1049, 2014.