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Detecting community structure in complex networks using an interaction optimization process

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  • Kim, Paul
  • Kim, Sangwook

Abstract

Most complex networks contain community structures. Detecting these community structures is important for understanding and controlling the networks. Most community detection methods use network topology and edge density to identify optimal communities; however, these methods have a high computational complexity and are sensitive to network forms and types. To address these problems, in this paper, we propose an algorithm that uses an interaction optimization process to detect community structures in complex networks. This algorithm efficiently searches the candidates of optimal communities by optimizing the interactions of the members within each community based on the concept of greedy optimization. During this process, each candidate is evaluated using an interaction-based community model. This model quickly and accurately measures the difference between the quantity and quality of intra- and inter-community interactions. We test our algorithm on several benchmark networks with known community structures that include diverse communities detected by other methods. Additionally, after applying our algorithm to several real-world complex networks, we compare our algorithm with other methods. We find that the structure quality and coverage results achieved by our algorithm surpass those of the other methods.

Suggested Citation

  • Kim, Paul & Kim, Sangwook, 2017. "Detecting community structure in complex networks using an interaction optimization process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 525-542.
  • Handle: RePEc:eee:phsmap:v:465:y:2017:i:c:p:525-542
    DOI: 10.1016/j.physa.2016.08.012
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    References listed on IDEAS

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    1. Kim, Paul & Kim, Sangwook, 2015. "Detecting overlapping and hierarchical communities in complex network using interaction-based edge clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 46-56.
    2. Yong-Yeol Ahn & James P. Bagrow & Sune Lehmann, 2010. "Link communities reveal multiscale complexity in networks," Nature, Nature, vol. 466(7307), pages 761-764, August.
    3. Réka Albert & Hawoong Jeong & Albert-László Barabási, 1999. "Diameter of the World-Wide Web," Nature, Nature, vol. 401(6749), pages 130-131, September.
    4. Dongxiao He & Di Jin & Carlos Baquero & Dayou Liu, 2014. "Link Community Detection Using Generative Model and Nonnegative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-10, January.
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