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Detecting overlapping and hierarchical communities in complex network using interaction-based edge clustering

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  • Kim, Paul
  • Kim, Sangwook

Abstract

Most community detection methods use network topology and edge density to identify optimal communities. However, in these methods, several objects that are connected by high weights may be decomposed into different communities, even when they intuitively belong to a single community. In this case, it is more effective to classify the objects into the same community because they perform important roles in controlling and understanding the network. To achieve this goal, in this paper, we propose a method of detecting optimal community structures in a complex network using interaction-based edge clustering. Our approach is to consider network topology as well as interaction density when identifying overlapping and hierarchical communities. Additionally, we measure the differences between the quantity and quality of intra- and inter-community interactions to evaluate the quality of the community structure. We test our method on several benchmark networks with known community structures. Additionally, after applying our method to several real-world complex networks, we evaluate our method through comparison with other methods. We find that the community quality and the overlap quality for our method surpass the results of the other methods.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:phsmap:v:417:y:2015:i:c:p:46-56
    DOI: 10.1016/j.physa.2014.09.035
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    References listed on IDEAS

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    1. 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.
    2. Gergely Palla & Albert-László Barabási & Tamás Vicsek, 2007. "Quantifying social group evolution," Nature, Nature, vol. 446(7136), pages 664-667, April.
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    Cited by:

    1. Canwei Liu & Xingye Deng & Tingqin He & Lei Chen & Guangyang Deng & Yuanyu Hu, 2023. "Multi-View Learning-Based Fast Edge Embedding for Heterogeneous Graphs," Mathematics, MDPI, vol. 11(13), pages 1-23, July.
    2. Zhou, Kuang & Martin, Arnaud & Pan, Quan, 2015. "A similarity-based community detection method with multiple prototype representation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 519-531.
    3. 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.

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