IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v447y2016icp482-492.html
   My bibliography  Save this article

A dynamic evolutionary clustering perspective: Community detection in signed networks by reconstructing neighbor sets

Author

Listed:
  • Chen, Jianrui
  • Wang, Hua
  • Wang, Lina
  • Liu, Weiwei

Abstract

Community detection in social networks has been intensively studied in recent years. In this paper, a novel similarity measurement is defined according to social balance theory for signed networks. Inter-community positive links are found and deleted due to their low similarity. The positive neighbor sets are reconstructed by this method. Then, differential equations are proposed to imitate the constantly changing states of nodes. Each node will update its state based on the difference between its state and average state of its positive neighbors. Nodes in the same community will evolve together with time and nodes in the different communities will evolve far away. Communities are detected ultimately when states of nodes are stable. Experiments on real world and synthetic networks are implemented to verify detection performance. The thorough comparisons demonstrate the presented method is more efficient than two acknowledged better algorithms.

Suggested Citation

  • Chen, Jianrui & Wang, Hua & Wang, Lina & Liu, Weiwei, 2016. "A dynamic evolutionary clustering perspective: Community detection in signed networks by reconstructing neighbor sets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 482-492.
    • Handle: RePEc:eee:phsmap:v:447:y:2016:i:c:p:482-492
    DOI: 10.1016/j.physa.2015.12.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711501033X
    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.2015.12.006?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. repec:cup:cbooks:9780511771576 is not listed on IDEAS
    2. Wu, Jianshe & Zhang, Long & Li, Yong & Jiao, Yang, 2016. "Partition signed social networks via clustering dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 568-582.
    3. Wu, Jianshe & Lu, Rui & Jiao, Licheng & Liu, Fang & Yu, Xin & Wang, Da & Sun, Bo, 2013. "Phase transition model for community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1287-1301.
    4. Easley,David & Kleinberg,Jon, 2010. "Networks, Crowds, and Markets," Cambridge Books, Cambridge University Press, number 9780521195331, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ke Hu & Ju Xiang & Yun-Xia Yu & Liang Tang & Qin Xiang & Jian-Ming Li & Yong-Hong Tang & Yong-Jun Chen & Yan Zhang, 2020. "Significance-based multi-scale method for network community detection and its application in disease-gene prediction," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-24, March.
    2. Ma, Yinghong & Zhu, Xiaoyu & Yu, Qinglin, 2019. "Clusters detection based leading eigenvector in signed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1263-1275.
    3. Zhu, Xiaoyu & Ma, Yinghong & Liu, Zhiyuan, 2018. "A novel evolutionary algorithm on communities detection in signed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 938-946.

    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. Wu, Jianshe & Zhang, Long & Li, Yong & Jiao, Yang, 2016. "Partition signed social networks via clustering dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 568-582.
    2. Blazquez-Soriano, Amparo & Ramos-Sandoval, Rosmery, 2022. "Information transfer as a tool to improve the resilience of farmers against the effects of climate change: The case of the Peruvian National Agrarian Innovation System," Agricultural Systems, Elsevier, vol. 200(C).
    3. Martin L. Weitzman, 2015. "A Voting Architecture for the Governance of Free-Driver Externalities, with Application to Geoengineering," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(4), pages 1049-1068, October.
    4. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    5. Guo Weilong & Minca Andreea & Wang Li, 2016. "The topology of overlapping portfolio networks," Statistics & Risk Modeling, De Gruyter, vol. 33(3-4), pages 139-155, December.
    6. Kobayashi, Teruyoshi & Takaguchi, Taro, 2018. "Identifying relationship lending in the interbank market: A network approach," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 20-36.
    7. Konstantinos Antoniadis & Kostas Zafiropoulos & Vasiliki Vrana, 2016. "A Method for Assessing the Performance of e-Government Twitter Accounts," Future Internet, MDPI, vol. 8(2), pages 1-18, April.
    8. Maness, Michael & Cirillo, Cinzia, 2016. "An indirect latent informational conformity social influence choice model: Formulation and case study," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 75-101.
    9. Lomi, Alessandro & Fonti, Fabio, 2012. "Networks in markets and the propensity of companies to collaborate: An empirical test of three mechanisms," Economics Letters, Elsevier, vol. 114(2), pages 216-220.
    10. Zhang, Xuxi & Liu, Xianping & Lewis, Frank L. & Wang, Xia, 2020. "Bipartite tracking consensus of nonlinear multi-agent systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    11. Bing Han & Liyan Yang, 2013. "Social Networks, Information Acquisition, and Asset Prices," Management Science, INFORMS, vol. 59(6), pages 1444-1457, June.
    12. Dimitrios Karamanis, 2022. "Defence partnerships, military expenditure, investment, and economic growth: an analysis in PESCO countries," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 173, Hellenic Observatory, LSE.
    13. Levent V. Orman, 2016. "Information markets over trust networks," Electronic Commerce Research, Springer, vol. 16(4), pages 529-551, December.
    14. Zhu, Yu-Xiao & Cao, Yan-Yan & Chen, Ting & Qiu, Xiao-Yan & Wang, Wei & Hou, Rui, 2018. "Crossover phenomena in growth pattern of social contagions with restricted contact," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 408-414.
    15. Pablo Galaso & Adrián Rodríguez Miranda & Sebastian Goinheix, 2018. "Local development, social capital and social network analysis: evidence from Uruguay," Revista de Estudios Regionales, Universidades Públicas de Andalucía, vol. 3, pages 137-163.
    16. Takahiro Ezaki & Naoki Masuda, 2017. "Reinforcement learning account of network reciprocity," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-8, December.
    17. Mariann Ollar & Marzena Rostek, 2011. "Information Aggregation and Innovation in Market Design," Working Papers 11-12, NET Institute.
    18. Mr. Jorge A Chan-Lau, 2017. "Variance Decomposition Networks: Potential Pitfalls and a Simple Solution," IMF Working Papers 2017/107, International Monetary Fund.
    19. Lillo, Felipe & Valdés, Rodrigo, 2016. "Dynamics of financial markets and transaction costs: A graph-based study," Research in International Business and Finance, Elsevier, vol. 38(C), pages 455-465.
    20. Usha Sridhar & Sridhar Mandyam, 2016. "Loan Allocation and Guarantee Structure for Group Borrower Networks in Microfinance," Studies in Microeconomics, , vol. 4(2), pages 100-114, December.

    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:447:y:2016:i:c:p:482-492. 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.