Skip to main content
SpringerLink
Log in

A method to detect communities with stability in social networks

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

We propose stable community detection (SCD), a framework to effectively identify stable communities in online social networks (OSNs). Our framework works by first enriching the input network with the mutual relationship estimation of all links and then discovering stable communities using a lumped Markov chain model. SCD has the advantage of handling the real model of OSNs with weighted reciprocity relationships. This approach is also supported by a key connection between the persistence probability of a community and its local topology.To certify the efficiency and stability of the discovered communities, we test SCD on both synthesized datasets and real-world social traces, including the NetHEPT collaboration, Foursquare, Twitter and Facebook social networks, in reference to the consensus of other state-of-the-art detection methods. Competitive experimental results confirm the quality and efficacy of the proposed framework on identifying stable communities in OSNs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Backstrom L, Leskovec J (2011) Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the fourth ACM international conference on Web search and data mining, WSDM’11. ACM, New York, pp 635–644. doi:10.1145/1935826.1935914

  • Barnes ER (1982) An algorithm for partitioning the nodes of a graph. SIAM J Algebraic Discrete Meth 3(4):541–550. doi:10.1137/0603056

    Article  MATH  Google Scholar 

  • Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):P10008 (2008)

  • Brandes U, Delling D, Gaertler M, Gorke R, Hoefer M, Nikoloski Z, Wagner D (2008) On modularity clustering. IEEE Trans Knowl Data Eng 20(2):172–188. doi:10.1109/TKDE.2007.190689

    Article  Google Scholar 

  • Chakraborty T, Srinivasan S, Ganguly N, Bhowmick S, Mukherjee A (2013) Constant communities in complex networks. Scientific reports 3

  • Chen W, Wang C, Wang Y (2010a) Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD

  • Chen W, Yuan Y, Zhang L (2010b) Scalable influence maximization in social networks under the linear threshold model. In: The 10th IEEE international conference on data mining, ICDM

  • Delvenne JCC, Yaliraki SN, Barahona M (2010) Stability of graph communities across time scales. In: Proceedings of the National Academy of Sciences of the United States of America 107(29):12755–12760. doi:10.1073/pnas.0903215107

  • Li T, Vanden-Eijnden E (2008) Optimal partition and effective dynamics of complex networks. In: Proceedings of the National Academy of Sciences of the United States of America 105(23):7907–7912

  • Fire M, Tenenboim L, Lesser O, Puzis R, Rokach L, Elovici Y (2011) Link prediction in social networks using computationally efficient topological features. In: SocialCom/PASSAT. IEEE, pp 73–80

  • Fortunato S (2010) Community detection in graphs. Phys Rept 486(3–5):75–174

    Article  MathSciNet  Google Scholar 

  • Fortunato S, Barthelemy M (2006) Resolution limit in community detection. Physics 0607100 104(1):8

  • Fortunato S, Barthélemy M (2007) Resolution limit in community detection. Proc Natl Acad Sci 104(1):36

    Article  Google Scholar 

  • Fortunato S, Lancichinetti A (2009) Community detection algorithms: a comparative analysis: invited presentation, extended abstract. In: Proceedings of the fourth international ICST conference on performance evaluation methodologies and tools, VALUETOOLS’09, pp 27:1–27:2 ICST

  • Garey MR, Johnson DS (1990) Computers and intractability. In: A guide to the theory of NP-completeness. W. H. Freeman & Co., New York

  • Hoffmann KH, Salamon P (2009) Bounding the lumping error in markov chain dynamics. Appl Math Lett 22(9):1471–1475

    Article  MathSciNet  MATH  Google Scholar 

  • Jamali M, Haffari G, Ester M (2010) Modeling the temporal dynamics of social rating networks using bidirectional effects of social relations and rating patterns. In: Proceedings of the 2010 IEEE international conference on data mining workshops, ICDMW’10. IEEE Computer Society, Washington, pp 344–351. doi:10.1109/ICDMW.2010.103

  • Kemeny JG, Hazleton M, Laurie J, Gerald L (1959) Finite mathematical structures, 1st edn. Prentice-Hall, Inc, Englewood Cliffs

  • Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: Proceedings of the 19th international conference on world wide web, WWW ’10, pp 591–600. ACM, New York. doi:10.1145/1772690.1772751

  • Lancichinetti A, Fortunato S (2012) Consensus clustering in complex networks. Scientific Reports, vol 2. doi:10.1038/srep00336

  • Lancichinetti A, Radicchi F, Ramasco JJ, Fortunato S (2011) Finding statistically significant communities in networks. PLoS ONE 6(4):e18961. doi:10.1371/journal.pone.0018961

  • Leskovec J, Huttenlocher D, Kleinberg J (2010) Predicting positive and negative links in online social networks. In: Proceedings of the 19th international conference on World wide web, WWW ’10. ACM, New York, pp. 641–650. doi:10.1145/1772690.1772756

  • Liben-Nowell D, Kleinberg J (2003) The link prediction problem for social networks. In: Proceedings of the twelfth international conference on Information and knowledge management, CIKM ’03. ACM, New York, pp. 556–559. doi:10.1145/956863.956972

  • Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416. doi:10.1007/s11222-007-9033-z

    Article  MathSciNet  Google Scholar 

  • Newman M (2003) Fast algorithm for detecting community structure in networks. Phys Rev E 69 (2003)

  • Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256

    Article  MathSciNet  MATH  Google Scholar 

  • Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103(23):8577–8582. doi:10.1073/pnas.0601602103

    Article  Google Scholar 

  • Newman MEJ, Leicht EA (2007) Mixture models and exploratory analysis in networks. Proc Natl Acad Sci USA 104(23):9564–9569

    Article  MATH  Google Scholar 

  • Nguyen NP, Dinh TN, Tokala S, Thai MT (2011) Overlapping communities in dynamic networks: their detection and mobile applications. In: Proceedings of the 17th annual international conference on mobile computing and networking, MobiCom ’11. ACM, New York, pp 85–96. doi:10.1145/2030613.2030624

  • Palla G, Barabasi AL, Vicsek T (2007) Quantifying social group evolution. Nature 446(7136):664–667. doi:10.1038/nature05670

    Article  Google Scholar 

  • Pásztor B, Mottola L, Mascolo C, Picco GP, Ellwood S, Macdonald D (2010) Selective reprogramming of mobile sensor networks through social community detection. In: Proceedings of the 7th European conference on wireless sensor networks, EWSN’10. Springer, Berlin, pp 178–193. doi:10.1007/978-3-642-11917-0_12

  • Piccardi C (2011) Finding and testing network communities by lumped markov chains. PLoS ONE 6(11):e27028. doi:10.1371/journal.pone.0027028

  • Reichardt J, Bornholdt S (2004) Detecting fuzzy community structures in complex networks with a potts model. Phys Rev Lett 93(21):218701

  • Rosvall M, Bergstrom CT (2010) Mapping change in large networks. PLoS ONE 5(1):e8694. doi:10.1371/journal.pone.0008694

    Article  Google Scholar 

  • Sean PM, Richard LT (2009) Markov chains and stochastic stability, 2nd edn. Cambridge University Press, Cambridge

  • Seifi M, Junier I, Rouquier JB, Iskrov S, Guillaume JL (2013) Stable community cores in complex networks. In: Menezes R, Evsukoff A, Gonzlez MC (eds) Complex networks, studies in computational intelligence, vol 424, pp 87–98. Springer, Berlin (2013). doi:10.1007/978-3-642-30287-9_10

  • Viswanath B, Mislove A, Cha M, Gummadi KP (2009) On the evolution of user interaction in facebook. In: 2nd ACM SIGCOMM workshop on social networks

  • Viswanath B, Post A, Gummadi KP, Mislove A (2010) An analysis of social network-based sybil defenses. In: Proceedings of the ACM SIGCOMM 2010 conference, SIGCOMM ’10. ACM, New York, pp. 363–374. doi:10.1145/1851182.1851226

  • Šíma J, Schaeffer SE (2006) On the np-completeness of some graph cluster measures. In: Proceedings of the 32nd conference on current trends in theory and practice of computer science, SOFSEM’06. Springer, Berlin, pp 530–537. doi:10.1007/11611257_51

  • Li Y, Zhang ZL, Bao J (2012) Mutual or unrequited love: identifying stable clusters in social networks with uni- and bi-directional links. In: LNCS WAW 2012 (2012, to appear)

Download references

Author information

Authors and Affiliations

  1. Department of Computer and Information Sciences, Towson University, 7800 York Road, Suite 406, Room 445, Towson, MD, 21252, USA

    Nam P. Nguyen

  2. Department of Computer and Information Science and Engineering, University of Florida, CSE Building, Room E555, P.O. Box 116120, Gainesville, FL, 32611-6120, USA

    Md Abdul Alim & My T. Thai

  3. Department of Computer Science, Virginia Commonwealth University, East Hall, Room E4244, P.O. Box 843019, Richmond, VA, 23284-3019, USA

    Thang N. Dinh

Authors
  1. Nam P. Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Md Abdul Alim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thang N. Dinh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. My T. Thai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nam P. Nguyen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen, N.P., Alim, M.A., Dinh, T.N. et al. A method to detect communities with stability in social networks. Soc. Netw. Anal. Min. 4, 224 (2014). https://doi.org/10.1007/s13278-014-0224-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-014-0224-2

Keywords

Search

Navigation