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Community detection in complex networks using structural similarity

Author

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  • Dabaghi Zarandi, Fataneh
  • Kuchaki Rafsanjani, Marjan

Abstract

These days, community detection is an important field to understand the topology and functions in the complex networks. In this article, we propose a novel Community Detection Algorithm based on Structural Similarity (CDASS) that executed in two consecutive phases. In the first phase, we randomly remove some low similarity edges. Therefore, the network graph is converted into several disconnected components that are considered as primary communities. In the following, the primary communities are merged in order to identify the final community structure close to real communities. In the second phase, we use an our identified evaluation function to select the best communities between overall random generated partitions. Finally, we evaluate CDASS algorithm using several scenarios extracted from artificial and real networks. The results, obtained from simulation with these scenarios, show that proposed algorithm detects communities with high accuracy close to optimal case and is applicable in the large and small network topologies.

Suggested Citation

  • Dabaghi Zarandi, Fataneh & Kuchaki Rafsanjani, Marjan, 2018. "Community detection in complex networks using structural similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 882-891.
  • Handle: RePEc:eee:phsmap:v:503:y:2018:i:c:p:882-891
    DOI: 10.1016/j.physa.2018.02.212
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    References listed on IDEAS

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    Cited by:

    1. Jianjun Cheng & Xing Su & Haijuan Yang & Longjie Li & Jingming Zhang & Shiyan Zhao & Xiaoyun Chen, 2019. "Neighbor Similarity Based Agglomerative Method for Community Detection in Networks," Complexity, Hindawi, vol. 2019, pages 1-16, May.
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    3. Ning-Ning Wang & Zhen Jin & Xiao-Long Peng, 2019. "Community Detection with Self-Adapting Switching Based on Affinity," Complexity, Hindawi, vol. 2019, pages 1-16, November.
    4. 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.
    5. Xiwei Bai & Daowei Liu & Jie Tan & Hongying Yang & Hengfeng Zheng, 2019. "Dynamic Identification of Critical Nodes and Regions in Power Grid Based on Spatio-Temporal Attribute Fusion of Voltage Trajectory," Energies, MDPI, vol. 12(5), pages 1-16, February.

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