IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v490y2018icp1344-1354.html
   My bibliography  Save this article

A community detection method based on local similarity and degree clustering information

Author

Listed:
  • Wang, Tao
  • Yin, Liyan
  • Wang, Xiaoxia

Abstract

Community detection is of great importance to understand the structures and functions of networks. In this paper, a novel algorithm is proposed based on local similarity and degree clustering information. Local similarity is employed to measure the similarity between nodes and their neighbors in order to form communities within which nodes are closely connected. Degree clustering information, a hybrid criterion combining local neighborhood ratio with degree ratio, make a large number of nodes with low degree to embrace a small amount of nodes with high degree. Furthermore, each node in small scale communities has the duty to try to connect the nodes with high degree to expand communities, and finally the optimal community structure can be obtained. Simulation results on real and artificial networks show that the proposed algorithm has the excellent performance in terms of accuracy.

Suggested Citation

  • Wang, Tao & Yin, Liyan & Wang, Xiaoxia, 2018. "A community detection method based on local similarity and degree clustering information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1344-1354.
    • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:1344-1354
    DOI: 10.1016/j.physa.2017.08.090
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117308051
    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.2017.08.090?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. L. Šubelj & M. Bajec, 2012. "Ubiquitousness of link-density and link-pattern communities in real-world networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 85(1), pages 1-11, January.
    2. 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.
    3. Cui, Yaozu & Wang, Xingyuan & Li, Junqiu, 2014. "Detecting overlapping communities in networks using the maximal sub-graph and the clustering coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 85-91.
    4. Wang, Tao & Wang, Hongjue & Wang, Xiaoxia, 2015. "A novel cosine distance for detecting communities in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 21-35.
    5. Cui, Yaozu & Wang, Xingyuan & Eustace, Justine, 2014. "Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 198-207.
    6. Duncan J. Watts, 2007. "A twenty-first century science," Nature, Nature, vol. 445(7127), pages 489-489, February.
    7. Jianbin Huang & Heli Sun & Yaguang Liu & Qinbao Song & Tim Weninger, 2011. "Towards Online Multiresolution Community Detection in Large-Scale Networks," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-11, August.
    8. Antonio Perianes-Rodríguez & Carlos Olmeda-Gómez & Félix Moya-Anegón, 2010. "Detecting, identifying and visualizing research groups in co-authorship networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 307-319, February.
    9. 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.
    10. Gong, Maoguo & Liu, Jie & Ma, Lijia & Cai, Qing & Jiao, Licheng, 2014. "Novel heuristic density-based method for community detection in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 71-84.
    11. Jin, Hong & Wang, Shuliang & Li, Chenyang, 2013. "Community detection in complex networks by density-based clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4606-4618.
    12. Li, Yafang & Jia, Caiyan & Yu, Jian, 2015. "A parameter-free community detection method based on centrality and dispersion of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 321-334.
    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. 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).
    2. Zhang, Weitong & Zhang, Rui & Shang, Ronghua & Li, Juanfei & Jiao, Licheng, 2019. "Application of natural computation inspired method in community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 130-150.
    3. Hesamipour, Sajjad & Balafar, Mohammad Ali, 2019. "A new method for detecting communities and their centers using the Adamic/Adar Index and game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    4. Maihami, Vafa & Yaghmaee, Farzin, 2018. "Automatic image annotation using community detection in neighbor images," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 123-132.
    5. Chun-Chih Lo & Kuo-Hsuan Hsu & Shen-Chien Chen & Chin-Shiuh Shieh & Mong-Fong Horng, 2023. "Periodic Behavioral Routine Discovery Based on Implicit Spatial Correlations for Smart Home," Mathematics, MDPI, vol. 11(3), pages 1-26, January.
    6. 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.
    7. Dabaghi Zarandi, Fataneh & Kuchaki Rafsanjani, Marjan, 2018. "Community detection in complex networks using structural similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 882-891.
    8. Cai, Biao & Wang, Yanpeng & Zeng, Lina & Hu, Yanmei & Li, Hongjun, 2020. "Edge classification based on Convolutional Neural Networks for community detection in complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    9. Wang, Tao & Chen, Shanshan & Wang, Xiaoxia & Wang, Jinfang, 2020. "Label propagation algorithm based on node importance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    10. Ning-Ning Wang & Zhen Jin & Xiao-Long Peng, 2019. "Community Detection with Self-Adapting Switching Based on Affinity," Complexity, Hindawi, vol. 2019, pages 1-16, November.

    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. Wang, Tao & Wang, Hongjue & Wang, Xiaoxia, 2015. "A novel cosine distance for detecting communities in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 21-35.
    2. Li, Yafang & Jia, Caiyan & Yu, Jian, 2015. "A parameter-free community detection method based on centrality and dispersion of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 321-334.
    3. Li, Yafang & Jia, Caiyan & Li, Jianqiang & Wang, Xiaoyang & Yu, Jian, 2018. "Enhanced semi-supervised community detection with active node and link selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 219-232.
    4. Nan, Dong-Yang & Yu, Wei & Liu, Xiao & Zhang, Yun-Peng & Dai, Wei-Di, 2018. "A framework of community detection based on individual labels in attribute networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 523-536.
    5. You, Tao & Cheng, Hui-Min & Ning, Yi-Zi & Shia, Ben-Chang & Zhang, Zhong-Yuan, 2016. "Community detection in complex networks using density-based clustering algorithm and manifold learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 221-230.
    6. 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.
    7. Hu, Fang & Liu, Yuhua, 2016. "A new algorithm CNM-Centrality of detecting communities based on node centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 138-151.
    8. 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.
    9. Zhang, Hong, 2015. "Moderate tolerance promotes tag-mediated cooperation in spatial Prisoner’s dilemma game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 52-61.
    10. Yazdanparast, Sakineh & Havens, Timothy C., 2017. "Modularity maximization using completely positive programming," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 20-32.
    11. 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.
    12. Zhou, Xu & Liu, Yanheng & Wang, Jian & Li, Chun, 2017. "A density based link clustering algorithm for overlapping community detection in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 65-78.
    13. Zhang, Xiaolei & Ren, Yibin & Huang, Baoxiang & Han, Yong, 2018. "Analysis of time-varying characteristics of bus weighted complex network in Qingdao based on boarding passenger volume," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 376-394.
    14. 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.
    15. Jing Wang & Jing Wang & Jingfeng Guo & Liya Wang & Chunying Zhang & Bin Liu, 2023. "Research Progress of Complex Network Modeling Methods Based on Uncertainty Theory," Mathematics, MDPI, vol. 11(5), pages 1-27, March.
    16. Darrin J. Griffin & San Bolkan & Jennifer L. Holmgren & Frank Tutzauer, 2016. "Central journals and authors in communication using a publication network," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 91-104, January.
    17. Xiang, Ju & Tang, Yan-Ni & Gao, Yuan-Yuan & Zhang, Yan & Deng, Ke & Xu, Xiao-Ke & Hu, Ke, 2015. "Multi-resolution community detection based on generalized self-loop rescaling strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 127-139.
    18. Chong Myung Park & Angelica Rodriguez & Jazmin Rubi Flete Gomez & Isahiah Erilus & Hayoung Kim Donnelly & Yanling Dai & Alexandra Oliver-Davila & Paul Trunfio & Cecilia Nardi & Kimberly A. S. Howard &, 2021. "Embedding Life Design in Future Readiness Efforts to Promote Collective Impact and Economically Sustainable Communities: Conceptual Frameworks and Case Example," Sustainability, MDPI, vol. 13(23), pages 1-17, November.
    19. Letchford, Adrian & Preis, Tobias & Moat, Helen Susannah, 2016. "The advantage of simple paper abstracts," Journal of Informetrics, Elsevier, vol. 10(1), pages 1-8.
    20. Zhang, Zhiwei & Wang, Zhenyu, 2015. "Mining overlapping and hierarchical communities in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 25-33.

    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:490:y:2018:i:c:p:1344-1354. 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.