IDEAS home Printed from https://ideas.repec.org/a/hin/complx/9764341.html
   My bibliography  Save this article

Effectively Detecting Communities by Adjusting Initial Structure via Cores

Author

Listed:
  • Mei Chen
  • Zhichong Yang
  • Xiaofang Wen
  • Mingwei Leng
  • Mei Zhang
  • Ming Li

Abstract

Community detection is helpful to understand useful information in real-world networks by uncovering their natural structures. In this paper, we propose a simple but effective community detection algorithm, called ACC, which needs no heuristic search but has near-linear time complexity. ACC defines a novel similarity which is different from most common similarity definitions by considering not only common neighbors of two adjacent nodes but also their mutual exclusive degree. According to this similarity, ACC groups nodes together to obtain the initial community structure in the first step. In the second step, ACC adjusts the initial community structure according to cores discovered through a new local density which is defined as the influence of a node on its neighbors. The third step expands communities to yield the final community structure. To comprehensively demonstrate the performance of ACC, we compare it with seven representative state-of-the-art community detection algorithms, on small size networks with ground-truth community structures and relatively big-size networks without ground-truth community structures. Experimental results show that ACC outperforms the seven compared algorithms in most cases.

Suggested Citation

  • Mei Chen & Zhichong Yang & Xiaofang Wen & Mingwei Leng & Mei Zhang & Ming Li, 2019. "Effectively Detecting Communities by Adjusting Initial Structure via Cores," Complexity, Hindawi, vol. 2019, pages 1-20, November.
  • Handle: RePEc:hin:complx:9764341
    DOI: 10.1155/2019/9764341
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2019/9764341.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/8503/2019/9764341.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2019/9764341?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
    ---><---

    References listed on IDEAS

    as
    1. Martin Rosvall & Carl T Bergstrom, 2011. "Multilevel Compression of Random Walks on Networks Reveals Hierarchical Organization in Large Integrated Systems," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-10, April.
    2. A. Tabrizi, Shayan & Shakery, Azadeh & Asadpour, Masoud & Abbasi, Maziar & Tavallaie, Mohammad Ali, 2013. "Personalized PageRank Clustering: A graph clustering algorithm based on random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(22), pages 5772-5785.
    3. Yong-Yeol Ahn & James P. Bagrow & Sune Lehmann, 2010. "Link communities reveal multiscale complexity in networks," Nature, Nature, vol. 466(7307), pages 761-764, August.
    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. Lovro Šubelj & Nees Jan van Eck & Ludo Waltman, 2016. "Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-23, April.
    2. Sun, Peng Gang, 2015. "Community detection by fuzzy clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 408-416.
    3. Amiri, Babak & Karimianghadim, Ramin, 2024. "A novel text clustering model based on topic modelling and social network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    4. Jo, Hang-Hyun & Moon, Eunyoung, 2016. "Dynamical complexity in the perception-based network formation model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 282-292.
    5. Chakraborty, Abhijit & Krichene, Hazem & Inoue, Hiroyasu & Fujiwara, Yoshi, 2019. "Characterization of the community structure in a large-scale production network in Japan," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 210-221.
    6. Tamás Nepusz & Tamás Vicsek, 2013. "Hierarchical Self-Organization of Non-Cooperating Individuals," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-9, December.
    7. Wu, Zhihao & Lin, Youfang & Wan, Huaiyu & Tian, Shengfeng & Hu, Keyun, 2012. "Efficient overlapping community detection in huge real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2475-2490.
    8. Rosa Rodriguez-Sánchez & J. A. García & J. Fdez-Valdivia, 2014. "Evolutionary games between subject categories," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 869-888, October.
    9. Michele Coscia & Ricardo Hausmann, 2015. "Evidence That Calls-Based and Mobility Networks Are Isomorphic," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-15, December.
    10. Daisuke Sato & Yuichi Ikeda & Shuichi Kawai & Maxmilian Schich, 2020. "The sustainability and the survivability of Kyoto’s traditional craft industry revealed from supplier-customer network," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-23, November.
    11. Amulyashree Sridhar & Sharvani GS & AH Manjunatha Reddy & Biplab Bhattacharjee & Kalyan Nagaraj, 2019. "The Eminence of Co-Expressed Ties in Schizophrenia Network Communities," Data, MDPI, vol. 4(4), pages 1-23, November.
    12. Badie, Reza & Aleahmad, Abolfazl & Asadpour, Masoud & Rahgozar, Maseud, 2013. "An efficient agent-based algorithm for overlapping community detection using nodes’ closeness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5231-5247.
    13. Hiromitsu Goto & Wataru Souma & Mari Jibu & Yuichi Ikeda, 2020. "Multilayer Network Analysis of the Drug Pipeline in the Global Pharmaceutical Industry," Papers 2003.04620, arXiv.org.
    14. Jean-Gabriel Young & Antoine Allard & Laurent Hébert-Dufresne & Louis J Dubé, 2015. "A Shadowing Problem in the Detection of Overlapping Communities: Lifting the Resolution Limit through a Cascading Procedure," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-19, October.
    15. Wang, Yuyao & Bu, Zhan & Yang, Huan & Li, Hui-Jia & Cao, Jie, 2021. "An effective and scalable overlapping community detection approach: Integrating social identity model and game theory," Applied Mathematics and Computation, Elsevier, vol. 390(C).
    16. Le Ou-Yang & Dao-Qing Dai & Xiao-Fei Zhang, 2013. "Protein Complex Detection via Weighted Ensemble Clustering Based on Bayesian Nonnegative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-18, May.
    17. Alessandro Tavoni & Simon Levin, 2014. "Managing the climate commons at the nexus of ecology, behaviour and economics," Nature Climate Change, Nature, vol. 4(12), pages 1057-1063, December.
    18. Bech, Morten L. & Bergstrom, Carl T. & Rosvall, Martin & Garratt, Rodney J., 2015. "Mapping change in the overnight money market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 44-51.
    19. Yian Yin & Yuxiao Dong & Kuansan Wang & Dashun Wang & Benjamin F. Jones, 2022. "Public use and public funding of science," Nature Human Behaviour, Nature, vol. 6(10), pages 1344-1350, October.
    20. Abhijit Chakraborty & Yuichi Kichikawa & Takashi Iino & Hiroshi Iyetomi & Hiroyasu Inoue & Yoshi Fujiwara & Hideaki Aoyama, 2018. "Hierarchical communities in the walnut structure of the Japanese production network," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-25, August.

    More about this item

    Statistics

    Access and download statistics

    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:hin:complx:9764341. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.