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Community detection via measuring the strength between nodes for dynamic networks

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  • Yang, Kai
  • Guo, Qiang
  • Liu, Jian-Guo

Abstract

The detection of community structure for dynamic social networks is significant for understanding evolution features of collective behaviors. In this paper, we present community detection method based on nonnegative matrix factorization for dynamic networks considering the strength between nodes. The basic idea of this algorithm is that node pairs with stronger connection strength have more possibility to be grouped into the same community. Firstly, we build weighted networks by calculating the embeddedness Et and dispersion Dt between each pair of nodes to measure the strength of the relationships at each timestamp t. Then we construct a node strength matrix in which each element represents the connection strength of a pair of nodes. Combining the structural information at previous timestamp, the nonnegative matrix factorization method is used to detect the community structure for the dynamic networks. Finally, the experiments for two synthetic networks show that when considering the previous information, the accuracy of our algorithm improve 0.3425, 0.5191 for the first synthetic networks. For the second synthetic networks, the accuracy of our algorithm is also improved. Furthermore, we compare the other two algorithms, the results show that our algorithms perform better than other algorithms on the both synthetic networks. Our work may be helpful for providing a new perspective that we detect community structures for dynamic networks.

Suggested Citation

  • Yang, Kai & Guo, Qiang & Liu, Jian-Guo, 2018. "Community detection via measuring the strength between nodes for dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 256-264.
    • Handle: RePEc:eee:phsmap:v:509:y:2018:i:c:p:256-264
    DOI: 10.1016/j.physa.2018.06.038
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    References listed on IDEAS

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    1. Pan, Ying & Li, De-Hua & Liu, Jian-Guo & Liang, Jing-Zhang, 2010. "Detecting community structure in complex networks via node similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2849-2857.
    2. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    3. Li-Ying Tang & Sheng-Nan Li & Jian-Hong Lin & Qiang Guo & Jian-Guo Liu, 2016. "Community structure detection based on the neighbor node degree information," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 27(04), pages 1-11, April.
    4. Liu, Jian & Liu, Tingzhan, 2010. "Detecting community structure in complex networks using simulated annealing with k-means algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(11), pages 2300-2309.
    5. Sinan Aral & Dylan Walker, 2014. "Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment," Management Science, INFORMS, vol. 60(6), pages 1352-1370, June.
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    Cited by:

    1. Zhao, Zi-Juan & Guo, Qiang & Yu, Kai & Liu, Jian-Guo, 2020. "Identifying influential nodes for the networks with community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    2. Zhan, Weihua & Deng, Lei & Guan, Jihong & Niu, Jun & Sun, Dechao, 2020. "Revealing dynamic communities in networks using genetic algorithm with merge and split operators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).

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