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The independence of the centrality for community detection

Author

Listed:
  • Jin Lei

    (Beijing University of Posts and Telecommunications, Electronic Engineering Institute, Beijing 100876, China)

  • Wang Xiao Juan

    (Beijing University of Posts and Telecommunications, Electronic Engineering Institute, Beijing 100876, China)

  • Zhang Yong

    (Beijing University of Posts and Telecommunications, Electronic Engineering Institute, Beijing 100876, China)

Abstract

Community detection is significative in the complex network. This paper focuses on community detection based on clustering algorithms. We tend to find out the central nodes of the communities by centrality algorithms. Firstly, we define the distance between nodes using similarity. Then, a new centrality measuring the local density of nodes is put forward. Combining the independence of the centrality, the nodes in the network can be divided into four classes. Leveraging the product of centrality and independence, the central nodes in the network are easily identified. We also find that we can distinguish bridge nodes from central nodes using centrality and independence. This research designs a community detection algorithm combining centrality and independence. Simulation results reveal that our centrality is more effective than existing centralities in measuring local density and identifying community centers. Compared with other community detection algorithms, results prove the effectiveness of our algorithm. This paper just shows one application of independence of the centrality. There may be more useful applications of it.

Suggested Citation

  • Jin Lei & Wang Xiao Juan & Zhang Yong, 2018. "The independence of the centrality for community detection," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 29(07), pages 1-14, July.
    • Handle: RePEc:wsi:ijmpcx:v:29:y:2018:i:07:n:s0129183118500602
    DOI: 10.1142/S0129183118500602
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