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A graph clustering method for community detection in complex networks

Author

Listed:
  • Zhou, HongFang
  • Li, Jin
  • Li, JunHuai
  • Zhang, FaCun
  • Cui, YingAn

Abstract

Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster.

Suggested Citation

  • Zhou, HongFang & Li, Jin & Li, JunHuai & Zhang, FaCun & Cui, YingAn, 2017. "A graph clustering method for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 551-562.
    • Handle: RePEc:eee:phsmap:v:469:y:2017:i:c:p:551-562
    DOI: 10.1016/j.physa.2016.11.015
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    References listed on IDEAS

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    1. Eustace, Justine & Wang, Xingyuan & Cui, Yaozu, 2015. "Overlapping community detection using neighborhood ratio matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 510-521.
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    3. Liu, Xu & Forrest, Jeffrey Yi-Lin & Luo, Qiang & Yi, Dong-Yun, 2012. "Detecting community structure using biased random merging," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1797-1810.
    4. Wu, Jianshe & Hou, Yunting & Jiao, Yang & Li, Yong & Li, Xiaoxiao & Jiao, Licheng, 2015. "Density shrinking algorithm for community detection with path based similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 218-228.
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    Citations

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    Cited by:

    1. Hao Long & Xiao-Wei Liu, 2019. "A Unified Community Detection Algorithm In Large-Scale Complex Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-19, May.
    2. Hesamipour, Sajjad & Balafar, Mohammad Ali, 2019. "A new method for detecting communities and their centers using the Adamic/Adar Index and game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    3. 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.
    4. Zhang, Yuan & Li, Lu & Zhang, Wenbo & Cheng, Qixiu, 2022. "GATC and DeepCut: Deep spatiotemporal feature extraction and clustering for large-scale transportation network partition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    5. Agrawal, Smita & Patel, Atul, 2021. "SAG Cluster: An unsupervised graph clustering based on collaborative similarity for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    6. Ehsan Ardjmand & William A. Young II & Najat E. Almasarwah, 2021. "Detecting Community Structures Within Complex Networks Using a Discrete Unconscious Search Algorithm," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 12(2), pages 15-32, April.

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