IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v603y2022ics0378437122004782.html
   My bibliography  Save this article

Detecting network communities via greedy expanding based on local superiority index

Author

Listed:
  • Zhu, Junfang
  • Ren, Xuezao
  • Ma, Peijie
  • Gao, Kun
  • Wang, Bing-Hong
  • Zhou, Tao

Abstract

Community detection is a significant and challenging task in network science. Nowadays, plenty of attention has been paid on local methods for community detection. Greedy expanding is a popular and efficient class of local algorithms, which typically starts from some selected central nodes and expands those nodes to obtain provisional communities by optimizing a certain quality function. In this paper, we propose a novel index, called local superiority index (LSI), to identify central nodes. In the process of expansion, we use a fitness function to estimate the quality of provisional communities and ensure that all provisional communities must be weak communities. Evaluation based on the normalized mutual information suggests: (1) LSI is superior to the global maximal degree index and the local maximal degree index on most considered networks; (2) The proposed greedy algorithm based on LSI is better than some state-of-the-art algorithms on most considered networks.

Suggested Citation

  • Zhu, Junfang & Ren, Xuezao & Ma, Peijie & Gao, Kun & Wang, Bing-Hong & Zhou, Tao, 2022. "Detecting network communities via greedy expanding based on local superiority index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    • Handle: RePEc:eee:phsmap:v:603:y:2022:i:c:s0378437122004782
    DOI: 10.1016/j.physa.2022.127722
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122004782
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.127722?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, Qiong & Wu, Ting-Ting & Fang, Ming, 2013. "Detecting local community structures in complex networks based on local degree central nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(3), pages 529-537.
    2. Pablo M. Gleiser & Leon Danon, 2003. "Community Structure In Jazz," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 565-573.
    3. Shang, Ronghua & Zhang, Weitong & Jiao, Licheng & Stolkin, Rustam & Xue, Yu, 2017. "A community integration strategy based on an improved modularity density increment for large-scale networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 471-485.
    4. Pan, Ying & Li, De-Hua & Liu, Jian-Guo & Liang, Jing-Zhang, 2010. "Detecting community structure in complex networks via node similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2849-2857.
    5. Shang, Ronghua & Zhang, Weitong & Zhang, Jingwen & Feng, Jie & Jiao, Licheng, 2022. "Local community detection based on higher-order structure and edge information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    6. Liu, Jian & Liu, Tingzhan, 2010. "Detecting community structure in complex networks using simulated annealing with k-means algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(11), pages 2300-2309.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Wu, Tao & Chen, Leiting & Zhong, Linfeng & Xian, Xingping, 2017. "Predicting the evolution of complex networks via similarity dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 662-672.
    3. Zhang, Weitong & Zhang, Rui & Shang, Ronghua & Li, Juanfei & Jiao, Licheng, 2019. "Application of natural computation inspired method in community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 130-150.
    4. Chen, Ling-Jiao & Zhang, Zi-Ke & Liu, Jin-Hu & Gao, Jian & Zhou, Tao, 2017. "A vertex similarity index for better personalized recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 607-615.
    5. Shang, Ronghua & Luo, Shuang & Zhang, Weitong & Stolkin, Rustam & Jiao, Licheng, 2016. "A multiobjective evolutionary algorithm to find community structures based on affinity propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 203-227.
    6. 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.
    7. Gong, Maoguo & Ma, Lijia & Zhang, Qingfu & Jiao, Licheng, 2012. "Community detection in networks by using multiobjective evolutionary algorithm with decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 4050-4060.
    8. Laassem, Brahim & Idarrou, Ali & Boujlaleb, Loubna & Iggane, M’bark, 2022. "Label propagation algorithm for community detection based on Coulomb’s law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    9. Gao, Jian & Zhou, Tao, 2017. "Evaluating user reputation in online rating systems via an iterative group-based ranking method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 546-560.
    10. Leila M Naeni & Hugh Craig & Regina Berretta & Pablo Moscato, 2016. "A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-27, August.
    11. Xiang, Ju & Hu, Tao & Zhang, Yan & Hu, Ke & Li, Jian-Ming & Xu, Xiao-Ke & Liu, Cui-Cui & Chen, Shi, 2016. "Local modularity for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 451-459.
    12. Dabaghi Zarandi, Fataneh & Kuchaki Rafsanjani, Marjan, 2018. "Community detection in complex networks using structural similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 882-891.
    13. Yang, Kai & Guo, Qiang & Liu, Jian-Guo, 2018. "Community detection via measuring the strength between nodes for dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 256-264.
    14. Huang, Chung-Yuan & Chin, Wei-Chien-Benny & Fu, Yu-Hsiang & Tsai, Yu-Shiuan, 2019. "Beyond bond links in complex networks:Local bridges, global bridges and silk links," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    15. Sukeda, Issey & Miyauchi, Atsushi & Takeda, Akiko, 2023. "A study on modularity density maximization: Column generation acceleration and computational complexity analysis," European Journal of Operational Research, Elsevier, vol. 309(2), pages 516-528.
    16. Zhao, Zi-Juan & Guo, Qiang & Yu, Kai & Liu, Jian-Guo, 2020. "Identifying influential nodes for the networks with community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    17. Ai, Jun & Cai, Yifang & Su, Zhan & Zhang, Kuan & Peng, Dunlu & Chen, Qingkui, 2022. "Predicting user-item links in recommender systems based on similarity-network resource allocation," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    18. Yanmei Hu & Bo Yang & Bin Duo & Xing Zhu, 2022. "Exhaustive Exploitation of Local Seeding Algorithms for Community Detection in a Unified Manner," Mathematics, MDPI, vol. 10(15), pages 1-30, August.
    19. Yan, Chao & Chang, Zhenhai, 2020. "Modularized convex nonnegative matrix factorization for community detection in signed and unsigned networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    20. Dongming Chen & Mingshuo Nie & Jiarui Yan & Dongqi Wang & Qianqian Gan, 2022. "Network Representation Learning Algorithm Based on Complete Subgraph Folding," Mathematics, MDPI, vol. 10(4), pages 1-13, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:603:y:2022:i:c:s0378437122004782. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.