IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v603y2022ics0378437122005234.html
   My bibliography  Save this article

Direction matters in complex networks: A theoretical and applied study for greedy modularity optimization

Author

Listed:
  • Dugué, Nicolas
  • Perez, Anthony

Abstract

Many real-world systems can be modeled as directed networks, such as transportation, social, collaboration or vocabulary networks. However, direction is often neglected or even ignored in community detection algorithms. This is in particular the case on large networks, since there are only a few scalable algorithms at the time. One of the most used scalable algorithm, Louvain’s algorithm, is based on modularity maximization and commonly used for directed networks by forgetting direction. We show that this oversimplification in the modeling process may alter the quality of the results both theoretically and practically. Moreover, we introduced in a complementary version of this work the Directed Louvain algorithm based on directed modularity that found various successful applications that enlighten the importance of direction when detecting communities. We hence propose an overview of selected applications within some of the aforementioned fields. We hope that this study will encourage researchers to use directed modularity whenever it is relevant.

Suggested Citation

  • Dugué, Nicolas & Perez, Anthony, 2022. "Direction matters in complex networks: A theoretical and applied study for greedy modularity optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    • Handle: RePEc:eee:phsmap:v:603:y:2022:i:c:s0378437122005234
    DOI: 10.1016/j.physa.2022.127798
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122005234
    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.2022.127798?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. Angelo Furno & Nour-Eddin El Faouzi & Rajesh Sharma & Eugenio Zimeo, 2021. "Graph-based ahead monitoring of vulnerabilities in large dynamic transportation networks," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-35, March.
    2. Akshat Singhal & Song Cao & Christopher Churas & Dexter Pratt & Santo Fortunato & Fan Zheng & Trey Ideker, 2020. "Multiscale community detection in Cytoscape," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-10, October.
    3. Ivan Blekanov & Svetlana S. Bodrunova & Askar Akhmetov, 2021. "Detection of Hidden Communities in Twitter Discussions of Varying Volumes," Future Internet, MDPI, vol. 13(11), pages 1-17, November.
    4. Qing Ping & Chaomei Chen, 2018. "LitStoryTeller+: an interactive system for multi-level scientific paper visual storytelling with a supportive text mining toolbox," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1887-1944, September.
    5. Andrea Lancichinetti & Filippo Radicchi & José J Ramasco & Santo Fortunato, 2011. "Finding Statistically Significant Communities in Networks," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-18, April.
    6. Bojan Evkoski & Igor Mozetič & Nikola Ljubešić & Petra Kralj Novak, 2021. "Community evolution in retweet networks," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-21, September.
    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. 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. Gregory, Steve, 2012. "Ordered community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2752-2763.
    3. 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.
    4. Greg Morrison & L Mahadevan, 2012. "Discovering Communities through Friendship," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    5. Jiang, Yawen & Jia, Caiyan & Yu, Jian, 2013. "An efficient community detection method based on rank centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2182-2194.
    6. 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.
    7. 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).
    8. Fu, Xianghua & Liu, Liandong & Wang, Chao, 2013. "Detection of community overlap according to belief propagation and conflict," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 941-952.
    9. Dafne E. van Kuppevelt & Rena Bakhshi & Eelke M. Heemskerk & Frank W. Takes, 2022. "Community membership consistency applied to corporate board interlock networks," Journal of Computational Social Science, Springer, vol. 5(1), pages 841-860, May.
    10. Jiang, Zhongzhou & Liu, Jing & Wang, Shuai, 2016. "Traveling salesman problems with PageRank Distance on complex networks reveal community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 293-302.
    11. Kyle F Davis & Paolo D'Odorico & Francesco Laio & Luca Ridolfi, 2013. "Global Spatio-Temporal Patterns in Human Migration: A Complex Network Perspective," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-8, January.
    12. Rodica Ioana Lung & Camelia Chira & Anca Andreica, 2014. "Game Theory and Extremal Optimization for Community Detection in Complex Dynamic Networks," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
    13. Fu, Jingcheng & Wu, Jianliang & Liu, Chuanjian & Xu, Jin, 2016. "Leaders in communities of real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 428-441.
    14. Lan Huang & Guishen Wang & Yan Wang & Enrico Blanzieri & Chao Su, 2013. "Link Clustering with Extended Link Similarity and EQ Evaluation Division," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-18, June.
    15. Raghvendra Mall & Rocco Langone & Johan A K Suykens, 2014. "Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-18, June.
    16. 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.
    17. Li Wang & Jiang Wang & Yuanjun Bi & Weili Wu & Wen Xu & Biao Lian, 2014. "Noise-tolerance community detection and evolution in dynamic social networks," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 600-612, October.
    18. Stefano Bianchini & Moritz Muller & Pierre Pelletier & Kevin Wirtz, 2021. "Global health science leverages established collaboration network to fight COVID-19," Papers 2102.00298, arXiv.org.
    19. Navakas, Robertas & Džiugys, Algis & Peters, Bernhard, 2014. "A community-detection based approach to identification of inhomogeneities in granular matter," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 312-331.
    20. Frank Havemann & Jochen Gläser & Michael Heinz & Alexander Struck, 2012. "Identifying Overlapping and Hierarchical Thematic Structures in Networks of Scholarly Papers: A Comparison of Three Approaches," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-12, March.

    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:s0378437122005234. 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.