IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i4p113-d787938.html
   My bibliography  Save this article

Multi-Layer Feature Fusion-Based Community Evolution Prediction

Author

Listed:
  • Zhao Wang

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
    These authors contributed equally to this work.)

  • Qingguo Xu

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
    These authors contributed equally to this work.)

  • Weimin Li

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

Abstract

Analyzing and predicting community evolution has many important applications in criminology, sociology, and other fields. In community evolution prediction, most of the existing research is simply calculating the features of the community, and then predicting the evolution event through the classifier. However, these methods do not consider the complex characteristics of community evolution, and only predict the community’s evolution from a single level. To solve these problems, this paper proposes an algorithm called multi-layer feature fusion-based community evolution prediction, which obtains features from the community layer and node layer. The final community feature is the fusion of the two layer features. At the node layer, this paper proposes a global and local-based role-extraction algorithm. This algorithm can effectively discover different roles in the community. In this way, we can distinguish the influence of nodes with different characteristics on the community evolution. At the community layer, this paper proposes to use the community hypergraph to obtain the inter-community interaction relationship. After all the features are obtained, this paper trains a classifier through these features and uses them in community evolution prediction. The experimental results show that the algorithm proposed in this paper is better than other algorithms in terms of prediction effect.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:4:p:113-:d:787938
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/4/113/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/14/4/113/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    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. Antoni Calvó-Armengol & Yves Zenou, 2004. "Social Networks And Crime Decisions: The Role Of Social Structure In Facilitating Delinquent Behavior," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(3), pages 939-958, August.
    4. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    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. Nicolò Pecora & Pablo Rovira Kaltwasser & Alessandro Spelta, 2016. "Discovering SIFIs in Interbank Communities," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-17, December.
    2. Hanbaek Lyu & Yacoub H. Kureh & Joshua Vendrow & Mason A. Porter, 2024. "Learning low-rank latent mesoscale structures in networks," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    3. Zhang, Hongli & Gao, Yang & Zhang, Yue, 2018. "Overlapping communities from dense disjoint and high total degree clusters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 286-298.
    4. 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).
    5. Abdolhosseini-Qomi, Amir Mahdi & Yazdani, Naser & Asadpour, Masoud, 2020. "Overlapping communities and the prediction of missing links in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    6. Eustace, Justine & Wang, Xingyuan & Cui, Yaozu, 2015. "Overlapping community detection using neighborhood ratio matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 510-521.
    7. 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.
    8. Ma, Xiaoke & Wang, Bingbo & Yu, Liang, 2018. "Semi-supervised spectral algorithms for community detection in complex networks based on equivalence of clustering methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 786-802.
    9. Gao, Yang & Zhang, Hongli & Zhang, Yue, 2019. "Overlapping community detection based on conductance optimization in large-scale networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 69-79.
    10. Gao, Yang & Zhang, Hongli & Zhang, Yue, 2019. "Overlapping communities from lines and triangles in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 455-466.
    11. Ma, Xiaoke & Gao, Lin & Yong, Xuerong & Fu, Lidong, 2010. "Semi-supervised clustering algorithm for community structure detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(1), pages 187-197.
    12. Wang, Xiao & Cao, Xiaochun & Jin, Di & Cao, Yixin & He, Dongxiao, 2016. "The (un)supervised NMF methods for discovering overlapping communities as well as hubs and outliers in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 22-34.
    13. Zhaoyang Li & Yuehan Yang, 2024. "A semi-orthogonal nonnegative matrix tri-factorization algorithm for overlapping community detection," Statistical Papers, Springer, vol. 65(6), pages 3601-3619, August.
    14. Nicolò Pecora & Alessandro Spelta, 2016. "Discovering SIFIs in interbank communities," DISCE - Working Papers del Dipartimento di Economia e Finanza def037, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    15. Rafael Teixeira & Mário Antunes & Diogo Gomes & Rui L. Aguiar, 2024. "Comparison of Semantic Similarity Models on Constrained Scenarios," Information Systems Frontiers, Springer, vol. 26(4), pages 1307-1330, August.
    16. Yekun Qin & Shanminhui Yin & Fang Liu, 2024. "Navigating Criminal Responsibility in the Digital Marketplace: Implications of Network-Neutral Help Behavior and Beyond-5G Networks in E-Commerce Transactions," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 10667-10695, September.
    17. Del Corso, Gianna M. & Romani, Francesco, 2019. "Adaptive nonnegative matrix factorization and measure comparisons for recommender systems," Applied Mathematics and Computation, Elsevier, vol. 354(C), pages 164-179.
    18. P Fogel & C Geissler & P Cotte & G Luta, 2022. "Applying separative non-negative matrix factorization to extra-financial data," Working Papers hal-03689774, HAL.
    19. Daron Acemoglu & Matthew O. Jackson, 2017. "Social Norms and the Enforcement of Laws," Journal of the European Economic Association, European Economic Association, vol. 15(2), pages 245-295.
    20. Wim Bernasco & Thomas de Graaff & Jan Rouwendal & Wouter Steenbeek, 2017. "Social Interactions and Crime Revisited: An Investigation Using Individual Offender Data in Dutch Neighborhoods," The Review of Economics and Statistics, MIT Press, vol. 99(4), pages 622-636, July.

    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:jftint:v:14:y:2022:i:4:p:113-:d:787938. 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.