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Knowledge and Information Systems

, Volume 59, Issue 1, pp 1–31 | Cite as

Fast detection of community structures using graph traversal in social networks

  • Partha BasuchowdhuriEmail author
  • Satyaki Sikdar
  • Varsha Nagarajan
  • Khusbu Mishra
  • Surabhi Gupta
  • Subhashis Majumder
Regular Paper
  • 128 Downloads

Abstract

Finding community structures in social networks is considered to be a challenging task as many of the proposed algorithms are computationally expensive and does not scale well for large graphs. Most of the community detection algorithms proposed till date are unsuitable for applications that would require detection of communities in real time, especially for massive networks. The Louvain method, which uses modularity maximization to detect clusters, is usually considered to be one of the fastest community detection algorithms even without any provable bound on its running time. We propose a novel graph traversal-based community detection framework, which not only runs faster than the Louvain method but also generates clusters of better quality for most of the benchmark datasets. We show that our algorithms run in \(O(|V| + |E|)\) time to create an initial cover before using modularity maximization to get the final cover.

Keywords

Community detection Influenced Neighbor Score Brokers Community nodes Communities 

Notes

Acknowledgements

We would like to thank the reviewers for their effort for thoroughly reading our paper and for suggesting valuable changes. We would also like to thank Upasana Dutta for pointing out and correcting errors in some of the figures and algorithms that appear in this work.

References

  1. 1.
    Ankerst M, Breunig MM, Kriegel HP, Sander J (1999) Optics: ordering points to identify the clustering structure. ACM SIGMOD Rec 28(2):49–60. ISSN 0163-5808.  https://doi.org/10.1145/304181.304187. http://portal.acm.org/citation.cfm?id=304187
  2. 2.
    Blondel VD, Guillaume JL, Lambiotte R, Mech ELJS (2008) Fast unfolding of communities in large networks. J Stat Mech  https://doi.org/10.1088/1742-5468/2008/10/P10008
  3. 3.
    Brandes U, Delling D, Gaertler M, Goerke R, Hoefer M, Nikoloski Z, Wagner D (2006) Maximizing modularity is hard. http://arxiv.org/abs/physics/0608255
  4. 4.
    Chen J, Zaiane OR, Goebel R (2009) A visual data mining approach to find overlapping communities in networks. In: Memon N, Alhajj R (eds) ASONAM. IEEE Computer Society, pp 338–343. ISBN 978-0-7695-3689-7. http://dblp.uni-trier.de/db/conf/asunam/asunam2009.html#ChenZG09a
  5. 5.
    Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70:066111CrossRefGoogle Scholar
  6. 6.
    Creusefond J, Largillier T, Peyronnet S (2017) A lexdfs-based approach on finding compact communities. In: Kaya M, Erdoǧan Ö, Rokne J (eds) From social data mining and analysis to prediction and community detection. Springer, Berlin, pp 141–177Google Scholar
  7. 7.
    Cui W, Xiao Y, Wang H, Wang W (2014) Local search of communities in large graphs. In: Proceedings of the 2014 ACM SIGMOD international conference on management of data. ACM, pp 991–1002Google Scholar
  8. 8.
    Fortunato S, Hric D (2016) Community detection in networks: a user guide. Phys Rep 659:1–44MathSciNetCrossRefGoogle Scholar
  9. 9.
    Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Good BH, De Montjoye YA, Clauset A (2010) Performance of modularity maximization in practical contexts. Phys Rev E 81(4):046106MathSciNetCrossRefGoogle Scholar
  11. 11.
    Granovetter MS (1973) The strength of weak ties. Am J Sociol 78(78):1360–1380CrossRefGoogle Scholar
  12. 12.
    Gregory S (2008) A fast algorithm to find overlapping communities in networks. In: Daelemans W, Goethals B, Morik K (eds) ECML/PKDD (1), volume 5211 of lecture notes in computer science. Springer, Berlin, pp 408–423. ISBN 978-3-540-87478-2Google Scholar
  13. 13.
    Klimt B, Yang Y (2004) Introducing the enron corpus. In: First conference on email and anti-spam (CEAS) proceedingsGoogle Scholar
  14. 14.
    Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(4):046110CrossRefGoogle Scholar
  15. 15.
    Leskovec J, Kleinberg JM, Faloutsos C (2007) Graph evolution: densification and shrinking diameters. TKDD.  https://doi.org/10.1145/1217299.1217301
  16. 16.
    Lin C, Ishwar P, Ding W (2017) Node embedding for network community discovery. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 4129–4133.  https://doi.org/10.1109/ICASSP.2017.7952933
  17. 17.
    Lusseau D, Newman MEJ (2004) Identifying the role that animals play in their social networks. Proc R Soc Lond Ser B Biol Sci 271:S477–S481CrossRefGoogle Scholar
  18. 18.
    Meghanathan N (2016) A greedy algorithm for neighborhood overlap-based community detection. Algorithms 9(1):8MathSciNetCrossRefGoogle Scholar
  19. 19.
    Nepusz T, Petroczi A, Negyessy L, Bazso F (2007) Fuzzy communities and the concept of bridgeness in complex networks. Phys Rev E 77:016107MathSciNetCrossRefGoogle Scholar
  20. 20.
    Newman MEJ (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69:066133.  https://doi.org/10.1103/PhysRevE.69.066133
  21. 21.
    Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103(23):8577–8582CrossRefGoogle Scholar
  22. 22.
    Nicosia V, Mangioni G, Carchiolo V, Malgeri M (2009) Extending the definition of modularity to directed graphs with overlapping communities. J Stat Mech Theory Exp 2009(03):P03024CrossRefGoogle Scholar
  23. 23.
    Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76:036106.  https://doi.org/10.1103/PhysRevE.76.036106 CrossRefGoogle Scholar
  24. 24.
    Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci USA 1118Google Scholar
  25. 25.
    Shen HW, Cheng XQ, Guo JF (2009) Quantifying and identifying the overlapping community structure in networks. J Stat Mech Theory Exp 2009(07):P07042. http://stacks.iop.org/1742-5468/2009/i=07/a=P07042
  26. 26.
    Wang X, Cui P, Wang J, Pei J, Zhu W, Yang S (2017) Community preserving network embedding. In: AAAI, pp 203–209Google Scholar
  27. 27.
    Wang Y, Cong G, Song G, Xie K (2010) Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’10. ACM, New York, NY, USA, pp 1039–1048. ISBN: 978-1-4503-0055-1.  https://doi.org/10.1145/1835804.1835935
  28. 28.
    Whang JJ, Gleich DF, Dhillon IS (2013) Overlapping community detection using seed set expansion. In: Proceedings of the 22nd ACM international conference on information & knowledge management, CIKM’13. ACM, New York, NY, USA, pp 2099–2108. ISBN: 978-1-4503-2263-8.  https://doi.org/10.1145/2505515.2505535
  29. 29.
    Xiang J, Tao H, Zhang Y, Ke H, Li J-M, Xiao-Ke X, Liu C-C, Chen S (2016) Local modularity for community detection in complex networks. Phys A Stat Mech Its Appl 443:451–459CrossRefGoogle Scholar
  30. 30.
    Yang J, Leskovec J (2012) Defining and evaluating network communities based on ground-truth. In: Zaki MJ, Siebes A, Yu JX, Goethals B, Webb GI, Wu X (eds) ICDM. IEEE Computer Society, pp 745–754. ISBN: 978-1-4673-4649-8. http://dblp.uni-trier.de/db/conf/icdm/icdm2012.html#YangL12
  31. 31.
    Zachary W (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33:452–473CrossRefGoogle Scholar
  32. 32.
    Zheng VW, Cavallari S, Cai H, Chang KC-C, Cambria E (2016) From node embedding to community embedding. arXiv preprint arXiv:1610.09950

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringHeritage Institute of TechnologyKolkataIndia
  2. 2.Department of Computer Science and EngineeringUniversity of Notre DameNotre DameUSA

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