VGHC: a variable granularity hierarchical clustering for community detection

Granular Computing volume 6, pages37–46(2021)Cite this article

Abstract

Hierarchical clustering is an effective method for community detection. This kind of method usually selects clustering threshold for layering, which will affect the performance of community detection. Most of them select uniform clustering threshold. It means that the hierarchical structure of the community is obtained by merging synchronously. Actually, the communities are merged asynchronously . So, how to capture the adaptive hierarchical structure of the community is a challenge. In this paper, we propose a variable granularity method VGHC to construct the hierarchical structure based on quotient space theory (QST). QST uses cut set to form a hierarchical structure which represents multi-granular spaces. Firstly, initial granules are built with the important nodes as the granule center. Secondly, we choose the median clustering coefficient of the lower layer as the cut set, and then lower layer can be clustering to higher layer. Finally, the extended modularity (EQ) or modularity (Q) is taken as the evaluation criterion to select a certain layer in the hierarchical structure as the community detection result. Some overlapping nodes exist because of clustering mechanism. To get a non-overlapping community structure, a local modularity optimization approach (LMO) is used to deal with overlapping nodes. Experiments on seven real-world networks demonstrate that our method is effective for community detection in networks compared with the state-of-the-art algorithms.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China [Grants Numbers 61602003, #61673020, #61876001]; the Provincial Natural Science Foundation of Anhui Province [Grants Number 1708085QF156]; the Innovation Zone Project Program for Science and Technology of China’s National Defense [Grant Number 2017-0001-863015-0009]; and the National Key Research and Development Program of China [Grant Number 2017YFB1401903].

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Affiliations

  1. School of Computer Science and Technology, Anhui University, Hefei, 230601, Anhui, People’s Republic of China

    Jie Chen, Yang Li, Shu Zhao & Yanping Zhang

  2. School of Computer Science and Technology, Hefei Normal University, Hefei, Anhui, People’s Republic of China

    Xuejie Yang

Authors
  1. Jie Chen
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  2. Yang Li
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  3. Xuejie Yang
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  4. Shu Zhao
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  5. Yanping Zhang
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Correspondence to Shu Zhao.

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Chen, J., Li, Y., Yang, X. et al. VGHC: a variable granularity hierarchical clustering for community detection. Granul. Comput. 6, 37–46 (2021). https://doi.org/10.1007/s41066-019-00195-1

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Keywords

  • Community detection
  • Hierarchical clustering
  • Variable granularity
  • Local modularity optimization approach