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Novel heuristic density-based method for community detection in networks

Author

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  • Gong, Maoguo
  • Liu, Jie
  • Ma, Lijia
  • Cai, Qing
  • Jiao, Licheng

Abstract

Recent years have witnessed a growing recognition on the community detection in networks. Diverse techniques have been devoted to uncovering community structures in complex networks and amongst which are the density-based methods. Density-based avenues are very popular in data clustering field. They rely on two parameters which are utilized by us to process the community detection problem. In this paper, a novel view to look deep into the network structure from the community level is tested and a heuristic density-based approach for community detection is put forward. In the proposed method, firstly, both of the two parameters are under consideration and all the possible parameter pairs are exploited. These parameter pairs produce all kinds of partitions through the classic method. Secondly, these partitions are processed by our proposed strategy consisting of classification, mergence, decomposition and recombination. After employing the proposed strategy, a community division with high quality is uncovered. Experiments on both synthetic and real-world networks demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Gong, Maoguo & Liu, Jie & Ma, Lijia & Cai, Qing & Jiao, Licheng, 2014. "Novel heuristic density-based method for community detection in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 71-84.
  • Handle: RePEc:eee:phsmap:v:403:y:2014:i:c:p:71-84
    DOI: 10.1016/j.physa.2014.01.043
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    References listed on IDEAS

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    1. Huang, Jianbin & Sun, Heli & Han, Jiawei & Feng, Boqin, 2011. "Density-based shrinkage for revealing hierarchical and overlapping community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2160-2171.
    2. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    3. 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.
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    Citations

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    Cited by:

    1. You, Tao & Cheng, Hui-Min & Ning, Yi-Zi & Shia, Ben-Chang & Zhang, Zhong-Yuan, 2016. "Community detection in complex networks using density-based clustering algorithm and manifold learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 221-230.
    2. Zhou, Xu & Liu, Yanheng & Wang, Jian & Li, Chun, 2017. "A density based link clustering algorithm for overlapping community detection in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 65-78.
    3. Li, Yafang & Jia, Caiyan & Yu, Jian, 2015. "A parameter-free community detection method based on centrality and dispersion of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 321-334.
    4. Ding, Jiajun & He, Xiongxiong & Yuan, Junqing & Chen, Yan & Jiang, Bo, 2018. "Community detection by propagating the label of center," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 675-686.
    5. Hu, Fang & Liu, Yuhua, 2016. "A new algorithm CNM-Centrality of detecting communities based on node centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 138-151.
    6. Wang, Tao & Yin, Liyan & Wang, Xiaoxia, 2018. "A community detection method based on local similarity and degree clustering information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1344-1354.
    7. Zhang, Beibei & Zhou, Yadong & Xu, Xiaoyan & Wang, Dai & Guan, Xiaohong, 2016. "Dynamic structure evolution of time-dependent network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 347-358.
    8. Shang, Ronghua & Luo, Shuang & Li, Yangyang & Jiao, Licheng & Stolkin, Rustam, 2015. "Large-scale community detection based on node membership grade and sub-communities integration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 279-294.
    9. Yazdanparast, Sakineh & Havens, Timothy C., 2017. "Modularity maximization using completely positive programming," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 20-32.

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