IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i15p2807-d882781.html
   My bibliography  Save this article

Exhaustive Exploitation of Local Seeding Algorithms for Community Detection in a Unified Manner

Author

Listed:
  • Yanmei Hu

    (College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China)

  • Bo Yang

    (School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Bin Duo

    (College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China)

  • Xing Zhu

    (College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China)

Abstract

Community detection is an essential task in network analysis and is challenging due to the rapid growth of network scales. Recently, discovering communities from the local perspective of some specified nodes called seeds, rather than requiring the global information of the entire network, has become an alternative approach to addressing this challenge. Some seeding algorithms have been proposed in the literature for finding seeds, but many of them require an excessive amount of effort because of the global information or intensive computation involved. In our study, we formally summarize a unified framework for local seeding by considering only the local information of each node. In particular, both popular local seeding algorithms and new ones are instantiated from this unified framework by adopting different centrality metrics. We categorize these local seeding algorithms into three classes and compare them experimentally on a number of networks. The experiments demonstrate that the degree-based algorithms usually select the fewest seeds, while the denseness-based algorithms, except the one with node mass as the centrality metric, select the most seeds; using the conductance of the egonet as the centrality metric performs best in discovering communities with good quality; the core-based algorithms perform best overall considering all the evaluation metrics; and among the core-based algorithms, the one with the Jaccard index works best. The experimental results also reveal that all the seeding algorithms perform poorly in large networks, which indicates that discovering communities in large networks is still an open problem that urgently needs to be addressed.

Suggested Citation

  • Yanmei Hu & Bo Yang & Bin Duo & Xing Zhu, 2022. "Exhaustive Exploitation of Local Seeding Algorithms for Community Detection in a Unified Manner," Mathematics, MDPI, vol. 10(15), pages 1-30, August.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2807-:d:882781
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/15/2807/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/15/2807/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Qiong & Wu, Ting-Ting & Fang, Ming, 2013. "Detecting local community structures in complex networks based on local degree central nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(3), pages 529-537.
    2. Deng, Zheng-Hong & Qiao, Hong-Hai & Gao, Ming-Yu & Song, Qun & Gao, Li, 2019. "Complex network community detection method by improved density peaks model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    3. Ding, Jiajun & He, Xiongxiong & Yuan, Junqing & Chen, Yan & Jiang, Bo, 2018. "Community detection by propagating the label of center," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 675-686.
    4. Jianjun Cheng & Wenbo Zhang & Haijuan Yang & Xing Su & Tao Ma & Xiaoyun Chen, 2020. "A Seed-Expanding Method Based on TOPSIS for Community Detection in Complex Networks," Complexity, Hindawi, vol. 2020, pages 1-14, March.
    5. Sun, Peng Gang & Wu, Xunlian & Quan, Yining & Miao, Qiguang, 2022. "Influence percolation method for overlapping community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    6. Garza, Sara E. & Schaeffer, Satu Elisa, 2019. "Community detection with the Label Propagation Algorithm: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    7. Shang, Ronghua & Zhang, Weitong & Zhang, Jingwen & Feng, Jie & Jiao, Licheng, 2022. "Local community detection based on higher-order structure and edge information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    8. Xiaofeng Wang & Gongshen Liu & Jianhua Li & Jan P Nees, 2017. "Locating Structural Centers: A Density-Based Clustering Method for Community Detection," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-23, January.
    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. Pir Noman Ahmad & Yuanchao Liu & Gauhar Ali & Mudasir Ahmad Wani & Mohammed ElAffendi, 2023. "Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data," Mathematics, MDPI, vol. 11(12), pages 1-23, June.

    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. Luo, Mengdi & Xu, Ying, 2022. "Community detection via network node vector label propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    2. Zhu, Junfang & Ren, Xuezao & Ma, Peijie & Gao, Kun & Wang, Bing-Hong & Zhou, Tao, 2022. "Detecting network communities via greedy expanding based on local superiority index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    3. Jianjun Cheng & Xing Su & Haijuan Yang & Longjie Li & Jingming Zhang & Shiyan Zhao & Xiaoyun Chen, 2019. "Neighbor Similarity Based Agglomerative Method for Community Detection in Networks," Complexity, Hindawi, vol. 2019, pages 1-16, May.
    4. Ding, Jiajun & He, Xiongxiong & Yuan, Junqing & Chen, Yan & Jiang, Bo, 2018. "Community detection by propagating the label of center," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 675-686.
    5. Jing Yang & Jun Wang & Mengyang Gao, 2023. "Community Evolution Analysis Driven by Tag Events: The Special Perspective of New Tags," Mathematics, MDPI, vol. 11(6), pages 1-18, March.
    6. Qiao, Honghai & Deng, Zhenghong & Li, Huijia & Hu, Jun & Song, Qun & Xia, Chengyi, 2021. "Complex networks from time series data allow an efficient historical stage division of urban air quality information," Applied Mathematics and Computation, Elsevier, vol. 410(C).
    7. Liu, Saisai & Xia, Zhengyou, 2020. "A two-stage BFS local community detection algorithm based on node transfer similarity and Local Clustering Coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    8. Garza, Sara E. & Schaeffer, Satu Elisa, 2019. "Community detection with the Label Propagation Algorithm: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    9. Aghaalizadeh, Saeid & Afshord, Saeid Taghavi & Bouyer, Asgarali & Anari, Babak, 2021. "A three-stage algorithm for local community detection based on the high node importance ranking in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    10. Liu, Qian & Wang, Jian & Zhao, Zhidan & Zhao, Na, 2022. "Relatively important nodes mining algorithm based on community detection and biased random walk with restart," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    11. Zhang, Yuan & Li, Lu & Zhang, Wenbo & Cheng, Qixiu, 2022. "GATC and DeepCut: Deep spatiotemporal feature extraction and clustering for large-scale transportation network partition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    12. Shang, Ronghua & Luo, Shuang & Li, Yangyang & Jiao, Licheng & Stolkin, Rustam, 2015. "Large-scale community detection based on node membership grade and sub-communities integration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 279-294.
    13. Wang, Benyu & Gu, Yijun & Zheng, Diwen, 2022. "Community detection in error-prone environments based on particle cooperation and competition with distance dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    14. Na Zhang & Yu Yang & Yujie Zheng & Jiafu Su, 2019. "Module partition of complex mechanical products based on weighted complex networks," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1973-1998, April.
    15. Shang, Ronghua & Zhang, Weitong & Zhang, Jingwen & Feng, Jie & Jiao, Licheng, 2022. "Local community detection based on higher-order structure and edge information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    16. Hosseini-Pozveh, Maryam & Ghorbanian, Maedeh & Tabaiyan, Maryam, 2022. "A label propagation-based method for community detection in directed signed social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    17. Wu, Xunlian & Zhang, Han & Quan, Yining & Miao, Qiguang & Sun, Peng Gang, 2023. "Graph embedding based on motif-aware feature propagation for community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    18. Dongming Chen & Mingshuo Nie & Jiarui Yan & Dongqi Wang & Qianqian Gan, 2022. "Network Representation Learning Algorithm Based on Complete Subgraph Folding," Mathematics, MDPI, vol. 10(4), pages 1-13, February.

    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:15:p:2807-:d:882781. 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.