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Markov-network based latent link analysis for community detection in social behavioral interactions

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Abstract

How to represent and discover social links from the perspective of implied behaviors, in particular latent links, is critical for social media analysis. In this paper, we discuss latent link analysis for community detection in social behavioral interactions. We adopt Markov network (MN) as the framework and propose the algorithm to discover latent links among social objects implied in their behavioral interactions without regard for the topological structures of social networks. First, starting from the frequent itemsets of the behavioral interactions, we propose the algorithm to construct the item-association Markov network (IAMN), which establishes the inherent relationship between frequent itemset and MN. Then, we propose the algorithm to detect communities by incorporating the concepts of k-clique and k-nearest neighbor set, as the typical application of the constructed IAMN Experimental results show the effectiveness and efficiency of the method proposed in this paper.

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Acknowledgements

This paper was supported by the National Natural Science Foundation of China (61472345, 61462056, 61562090, 61232002), Natural Science Foundation of Yunnan Province (2014FA023, 2014FA028), Program for the second Batch of Yunling Scholar of Yunnan Province (C6153001), Program for Excellent Young Talents, Yunnan University (WX173602), and China Postdoctoral Science Foundation (2016M592721).

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Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, China

    Weiyi Liu, Kun Yue, Hao Wu, Zhijian Zhang & Weipeng Huang

  2. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

    Xiaodong Fu

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  1. Weiyi Liu
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  2. Kun Yue
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  3. Hao Wu
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  4. Xiaodong Fu
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  5. Zhijian Zhang
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  6. Weipeng Huang
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Correspondence to Kun Yue.

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Liu, W., Yue, K., Wu, H. et al. Markov-network based latent link analysis for community detection in social behavioral interactions. Appl Intell 48, 2081–2096 (2018). https://doi.org/10.1007/s10489-017-1040-y

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