Abstract
Extracting ego-centered community from large social networks is a practical problem in many real applications. Previous work for ego-centered community detection usually focuses on topological properties of networks, which ignore the actual social attributes between nodes. In this paper, we formalize the ego-centered community detection problem in a unified factor graph model and employ a parameter learning algorithm to estimate the topic-level social influence, the social relationship strength between nodes as well as community structures of networks. Based on the unified model we can obtain more meaningful ego community compared with traditional methods. Experimental results on co-author network demonstrate the effectiveness and efficiency of the proposed approach.
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Jia, Y., Gao, Y., Yang, W., Huo, J., Shi, Y. (2014). A Novel Ego-Centered Academic Community Detection Approach via Factor Graph Model. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_28
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DOI: https://doi.org/10.1007/978-3-319-10840-7_28
Publisher Name: Springer, Cham
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