IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i6p970-d774029.html
   My bibliography  Save this article

Community Detection Based on Node Influence and Similarity of Nodes

Author

Listed:
  • Yanjie Xu

    (Software College, Northeastern University, Shenyang 110169, China)

  • Tao Ren

    (Software College, Northeastern University, Shenyang 110169, China)

  • Shixiang Sun

    (Software College, Northeastern University, Shenyang 110169, China)

Abstract

Community detection is a fundamental topic in network science, with a variety of applications. However, there are still fundamental questions about how to detect more realistic network community structures. To address this problem and considering the structure of a network, we propose an agglomerative community detection algorithm, which is based on node influence and the similarity of nodes. The proposed algorithm consists of three essential steps: identifying the central node based on node influence, selecting a candidate neighbor to expand the community based on the similarity of nodes, and merging the small community based on the similarity of communities. The performance and effectiveness of the proposed algorithm were tested on real and synthetic networks, and they were further evaluated through modularity and NMI anlaysis. The experimental results show that the proposed algorithm is effective in community detection and it is quite comparable to existing classic methods.

Suggested Citation

  • Yanjie Xu & Tao Ren & Shixiang Sun, 2022. "Community Detection Based on Node Influence and Similarity of Nodes," Mathematics, MDPI, vol. 10(6), pages 1-15, March.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:970-:d:774029
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/6/970/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/6/970/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    2. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    3. Ludo Waltman & Nees Eck, 2013. "A smart local moving algorithm for large-scale modularity-based community detection," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(11), pages 1-14, November.
    4. Chong Feng & Jianxu Ye & Jianlu Hu & Hui Lin Yuan & Honglei Xu, 2021. "Community Detection by Node Betweenness and Similarity in Complex Network," Complexity, Hindawi, vol. 2021, pages 1-13, July.
    5. Chen, Xiangtao & Li, Juan, 2019. "Community detection in complex networks using edge-deleting with restrictions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 181-194.
    6. Dong Wang & Jiexun Li & Kaiquan Xu & Yizhen Wu, 2017. "Sentiment community detection: exploring sentiments and relationships in social networks," Electronic Commerce Research, Springer, vol. 17(1), pages 103-132, March.
    7. Agrawal, Smita & Patel, Atul, 2021. "SAG Cluster: An unsupervised graph clustering based on collaborative similarity for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    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. Zhang, Yi & Wu, Mengjia & Miao, Wen & Huang, Lu & Lu, Jie, 2021. "Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies," Journal of Informetrics, Elsevier, vol. 15(4).
    2. Guo, Xue & Li, Weibo & Zhang, Hu & Tian, Tianhai, 2022. "Multi-likelihood methods for developing relationship networks using stock market data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    3. Zhao Wang & Qingguo Xu & Weimin Li, 2022. "Multi-Layer Feature Fusion-Based Community Evolution Prediction," Future Internet, MDPI, vol. 14(4), pages 1-20, April.
    4. Lutz Bornmann & Robin Haunschild & Sven E. Hug, 2018. "Visualizing the context of citations referencing papers published by Eugene Garfield: a new type of keyword co-occurrence analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 427-437, February.
    5. Eustace, Justine & Wang, Xingyuan & Cui, Yaozu, 2015. "Community detection using local neighborhood in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 665-677.
    6. Natalya Ivanova & Ekaterina Zolotova, 2023. "Landolt Indicator Values in Modern Research: A Review," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    7. Huan Wang & Chuang Ma & Han-Shuang Chen & Ying-Cheng Lai & Hai-Feng Zhang, 2022. "Full reconstruction of simplicial complexes from binary contagion and Ising data," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    8. Vinayak, & Raghuvanshi, Adarsh & kshitij, Avinash, 2023. "Signatures of capacity development through research collaborations in artificial intelligence and machine learning," Journal of Informetrics, Elsevier, vol. 17(1).
    9. Supriya Tiwari & Pallavi Basu, 2024. "Quasi-randomization tests for network interference," Papers 2403.16673, arXiv.org, revised Oct 2024.
    10. Xiao‐Bing Hu & Hang Li & XiaoMei Guo & Pieter H. A. J. M. van Gelder & Peijun Shi, 2019. "Spatial Vulnerability of Network Systems under Spatially Local Hazards," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 162-179, January.
    11. Nina Sakinah Ahmad Rofaie & Seuk Wai Phoong & Muzalwana Abdul Talib & Ainin Sulaiman, 2023. "Light-emitting diode (LED) research: A bibliometric analysis during 2003–2018," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 173-191, February.
    12. Anzhi Sheng & Qi Su & Aming Li & Long Wang & Joshua B. Plotkin, 2023. "Constructing temporal networks with bursty activity patterns," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    13. Giovanni Matteo & Pierfrancesco Nardi & Stefano Grego & Caterina Guidi, 2018. "Bibliometric analysis of Climate Change Vulnerability Assessment research," Environment Systems and Decisions, Springer, vol. 38(4), pages 508-516, December.
    14. Samrachana Adhikari & Beau Dabbs, 2018. "Social Network Analysis in R: A Software Review," Journal of Educational and Behavioral Statistics, , vol. 43(2), pages 225-253, April.
    15. Wang, Xiaojie & Slamu, Wushour & Guo, Wenqiang & Wang, Sixiu & Ren, Yan, 2022. "A novel semi local measure of identifying influential nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    16. Yi-Ming Wei & Jin-Wei Wang & Tianqi Chen & Bi-Ying Yu & Hua Liao, 2018. "Frontiers of Low-Carbon Technologies: Results from Bibliographic Coupling with Sliding Window," CEEP-BIT Working Papers 116, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    17. Jorge Peña & Yannick Rochat, 2012. "Bipartite Graphs as Models of Population Structures in Evolutionary Multiplayer Games," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-13, September.
    18. Loredana Canfora & Corrado Costa & Federico Pallottino & Stefano Mocali, 2021. "Trends in Soil Microbial Inoculants Research: A Science Mapping Approach to Unravel Strengths and Weaknesses of Their Application," Agriculture, MDPI, vol. 11(2), pages 1-21, February.
    19. Pirvu Daniela & Barbuceanu Mircea, 2016. "Recent Contributions Of The Statistical Physics In The Research Of Banking, Stock Exchange And Foreign Exchange Markets," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 2, pages 85-92, April.
    20. Lin, Dan & Wu, Jiajing & Xuan, Qi & Tse, Chi K., 2022. "Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).

    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:gam:jmathe:v:10:y:2022:i:6:p:970-:d:774029. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.