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Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density

Author

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  • Yubo Peng

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Bofeng Zhang

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Furong Chang

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

Abstract

Community detection plays an essential role in understanding network topology and mining underlying information. A bipartite network is a complex network with more important authenticity and applicability than a one-mode network in the real world. There are many communities in the network that present natural overlapping structures in the real world. However, most of the research focuses on detecting non-overlapping community structures in the bipartite network, and the resolution of the existing evaluation function for the community structure’s merits are limited. So, we propose a novel function for community detection and evaluation of the bipartite network, called community density D . And based on community density, a bipartite network community detection algorithm DSNE (Density Sub-community Node-pair Extraction) is proposed, which is effective for overlapping community detection from a micro point of view. The experiments based on artificially-generated networks and real-world networks show that the DSNE algorithm is superior to some existing excellent algorithms; in comparison, the community density (D) is better than the bipartite network’s modularity.

Suggested Citation

  • Yubo Peng & Bofeng Zhang & Furong Chang, 2021. "Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density," Future Internet, MDPI, vol. 13(4), pages 1-21, March.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:4:p:89-:d:527194
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    References listed on IDEAS

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    1. Zhou, Cangqi & Feng, Liang & Zhao, Qianchuan, 2018. "A novel community detection method in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1679-1693.
    2. Zhong-Yuan Zhang & Yong-Yeol Ahn, 2015. "Community detection in bipartite networks using weighted symmetric binary matrix factorization," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(09), pages 1-14.
    3. Wang, Xingyuan & Qin, Xiaomeng, 2016. "Asymmetric intimacy and algorithm for detecting communities in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 569-578.
    4. Feng, Liang & Zhou, Cangqi & Zhao, Qianchuan, 2019. "A spectral method to find communities in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 424-437.
    5. Jose C Nacher & Jean-Marc Schwartz, 2012. "Modularity in Protein Complex and Drug Interactions Reveals New Polypharmacological Properties," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-13, January.
    6. Sun, Hong-liang & Ch’ng, Eugene & Yong, Xi & Garibaldi, Jonathan M. & See, Simon & Chen, Duan-bing, 2018. "A fast community detection method in bipartite networks by distance dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 108-120.
    7. H. Jeong & S. P. Mason & A.-L. Barabási & Z. N. Oltvai, 2001. "Lethality and centrality in protein networks," Nature, Nature, vol. 411(6833), pages 41-42, May.
    8. Cui, Yaozu & Wang, Xingyuan, 2014. "Uncovering overlapping community structures by the key bi-community and intimate degree in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 7-14.
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    Cited by:

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