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Statistics of the efficiency in community detection by most similar node pairs

Author

Listed:
  • YunFeng Chang

    (College of Science, China Three Gorges University, YiChang, HuBei 443002, P. R. China)

  • Feng Gao

    (College of Science, China Three Gorges University, YiChang, HuBei 443002, P. R. China)

  • ZhiWei Zhang

    (College of Science, China Three Gorges University, YiChang, HuBei 443002, P. R. China)

  • ZhiYang Liu

    (College of Science, China Three Gorges University, YiChang, HuBei 443002, P. R. China)

  • Yi Li

    (College of Science, China Three Gorges University, YiChang, HuBei 443002, P. R. China)

  • YuDong Zhang

    (College of Science, China Three Gorges University, YiChang, HuBei 443002, P. R. China)

Abstract

Individuals that share common properties self-organize into communities. Therefore, community analysis is an important way to ascertain whether or not a complex system consists of sub-structures with different properties. In this paper, we give a two-level community structure analysis for the SSCI journal system by most similar node pairs. Five different strategies for the selection of node pairs are introduced. The efficiency is checked by normalized mutual information (NMI) technique. Statistical properties and comparisons of the community results show that both of the two level detections could give instructional information for the community structure in complex systems. Further comparisons of the five strategies show more indications. Firstly, it is always efficient to assign individuals with maximum similarity into the same community whether the interaction information is complete or not; secondly, rational random selection plays an important role in community diversity, and it is not a good idea to keep two much information for rational random selection; finally, random selection generates small-world community structure with no inside order. These results give valuable indication for efficient and stable community detection for huge complex systems with big interaction data.

Suggested Citation

  • YunFeng Chang & Feng Gao & ZhiWei Zhang & ZhiYang Liu & Yi Li & YuDong Zhang, 2019. "Statistics of the efficiency in community detection by most similar node pairs," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 30(05), pages 1-10, May.
    • Handle: RePEc:wsi:ijmpcx:v:30:y:2019:i:05:n:s0129183119500311
    DOI: 10.1142/S0129183119500311
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    References listed on IDEAS

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