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Events detection and community partition based on probabilistic snapshot for evolutionary social network

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Abstract

Most of the existing researches simply convert associations of nodes within the snapshot of the evolutionary social network to the weight of edges. However, because of the obvious Matthew effect existing in the interactions of nodes in the real social network, the association strength matrices extracted directly by snapshots are extremely uneven. This paper introduces a new evolutionary social network model. Firstly, we generate probabilistic snapshots of the evolutionary social network data. Afterwards, we use the probabilistic factor model to detect the variation points brought by network events. Finally we partition the network community based on snapshots with stable structures before and after the variation points. According to experimental results, our proposed probabilistic snapshot model is effective for network events detection and network community partition.

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Acknowledgments

This research is supported by the Science and Technology Program of Xiamen, China (No.3502Z20153026); Key Program of Science and Technology of Fujian, China (No. 2014H0044, 2015H0037); NSFC (No. 61402387); NSFC (No. 61402390).

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  1. Software School, Xiamen University, Xiamen, China

    Zhongnan Zhang, Lei Hu, Ming Qiu & Fangyuan Gao

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  1. Zhongnan Zhang
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  2. Lei Hu
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  3. Ming Qiu
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  4. Fangyuan Gao
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Correspondence to Zhongnan Zhang.

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Zhang, Z., Hu, L., Qiu, M. et al. Events detection and community partition based on probabilistic snapshot for evolutionary social network. Peer-to-Peer Netw. Appl. 10, 1272–1284 (2017). https://doi.org/10.1007/s12083-016-0427-6

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  • DOI: https://doi.org/10.1007/s12083-016-0427-6

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