Skip to main content
SpringerLink
Log in

A Generalized Framework for Detecting Social Network Communities by the Scanning Method

  • Conference paper
  • First Online:
Complex Networks and Their Applications VIII (COMPLEX NETWORKS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 881))

Included in the following conference series:

Abstract

With the popularity of social media, recognizing and analyzing social network patterns have become important issues. A society offers a wide variety of possible communities, such as schools, families, firms and many others. The study and detection of these communities have been popular among business and social science researchers. Under the Poisson random graph assumption, the scan statistics have been verified as a useful tool to determine the statistical significance of both structure and attribute clusters in networks. However, the Poisson random graph assumption may not be fulfilled in all networks. In this paper, we first generalize the scan statistics by considering the individual diversity of each edge. Then we construct the random connection probability model and the logit model, and demonstrate the effectiveness of the generalized method. Simulation studies show that the generalized method has better detection when compared to the existing methods.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc.: Ser. B 57(1), 289–300 (1995)

    MathSciNet  MATH  Google Scholar 

  2. Catanzaro, M., Boguñá, M., Pastor-Satorras, R.: Generation of uncorrelated random scale-free networks. Phys. Rev. E 71(2), 027103 (2005)

    Article  Google Scholar 

  3. Csermely, P.: Creative elements: network-based predictions of active centres in proteins and cellular and social networks. Trends Biochem. Sci. 33(12), 569–576 (2008)

    Article  Google Scholar 

  4. Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press, Cambridge (2010)

    Book  Google Scholar 

  5. Erdős, P., Rényi, A.: On random graphs. Publ. Math. Debr. 6, 290–297 (1959)

    MATH  Google Scholar 

  6. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  7. Fortunato, S., Barthélemy, M.: Resolution limit in community detection. Proc. Natl. Acad. Sci. 104(1), 36–41 (2007)

    Article  Google Scholar 

  8. Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40(1), 35–41 (1977)

    Article  Google Scholar 

  9. Guimera, R., Sales-Pardo, M., Amaral, L.A.N.: Modularity from fluctuations in random graphs and complex networks. Phys. Rev. E 70(2), 025101 (2004)

    Article  Google Scholar 

  10. Handcock, M.S., Raftery, A.E., Tantrum, J.M.: Model-based clustering for social networks. J. R. Stat. Soc.: Ser. A 170(2), 301–354 (2007)

    Article  MathSciNet  Google Scholar 

  11. Heard, N.A., Weston, D.J., Platanioti, K., Hand, D.J.: Bayesian anomaly detection methods for social networks. Ann. Appl. Stat. 4(2), 645–662 (2010)

    Article  MathSciNet  Google Scholar 

  12. Javadi, S.H.S., Khadivi, S., Shiri, M.E., Xu, J.: An ant colony optimization method to detect communities in social networks. In: Proceedings of Advances in Social Networks Analysis and Mining, pp. 200–203 (2014)

    Google Scholar 

  13. Hulldorff, M.: A spatial scan statistic. Commun. Stat.-Theory Methods 26(6), 1481–1496 (1997)

    Article  MathSciNet  Google Scholar 

  14. Lancichinetti, A., Radicchi, F., Ramasco, J.: Statistical significance of communities in networks. Phys. Rev. E 81(4), 046110 (2010)

    Article  Google Scholar 

  15. Liu, J., Liu, T.: Detecting community structure in complex networks using simulated annealing with k-means algorithms. Phys. A 389(11), 2300–2309 (2010)

    Article  Google Scholar 

  16. Magee, L.: \(R^2\) measures based on Wald and likelihood ratio joint significance tests. Am. Stat. 44(3), 250–253 (1990)

    MathSciNet  Google Scholar 

  17. Moody, J., White, D.R.: Structural cohesion and embeddedness: a hierarchical concept of social groups. Am. Sociol. Rev. 68(1), 103–127 (2003)

    Article  Google Scholar 

  18. Naus, J.I.: Approximations for distributions of scan statistics. J. Am. Stat. Assoc. 77(377), 177–183 (1982)

    Article  MathSciNet  Google Scholar 

  19. Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6), 066133 (2004)

    Article  Google Scholar 

  20. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)

    Article  Google Scholar 

  21. Patil, G.P., Taillie, C.: Upper level set scan statistic for detecting arbitrarily shaped hotspots. Environ. Ecol. Stat. 11(2), 183–197 (2004)

    Article  MathSciNet  Google Scholar 

  22. Perry, M.B., Michaelson, G.V., Ballard, M.A.: On the statistical detection of clusters in undirected networks. Comput. Stat. Data Anal. 68, 170–189 (2013)

    Article  MathSciNet  Google Scholar 

  23. Reddy, P.K., Kitsuregawa, M., Sreekanth, P., Rao, S.S.: A graph based approach to extract a neighborhood customer community for collaborative filtering. Databases Netw. Inf. Syst. 2544, 188–200 (2002)

    Article  Google Scholar 

  24. Robins, G., Pattison, P., Kalish, Y., Luster, D.: An introduction to exponential random graph (\(p^*\)) models for social networks. Soc. Netw. 29(2), 173–191 (2007)

    Article  Google Scholar 

  25. Strauss, D., Ikeda, M.: Pseudolikelihood estimation for social networks. J. Am. Stat. Assoc. 85(409), 204–212 (1990)

    Article  MathSciNet  Google Scholar 

  26. Tango, T., Takahashi, K.: A flexibly shaped spatial scan statistic for detecting clusters. Int. J. Health Geogr. 4(1), 11 (2005)

    Article  Google Scholar 

  27. Wang, B., Philips, J.M., Schreiber, R., Wilkinson, D.M., Mishra, N., Tarjan, R.: Spatial scan statistics for graph clustering. In: Proceedings of the 2008 SIAM International Conference on Data Mining, pp. 727–738 (2008)

    Google Scholar 

  28. Wang, T.C., Phoa, F.K.H.: A scanning method for detecting clustering pattern of both attribute and structure in social networks. Phys. A 445, 295–309 (2016)

    Article  Google Scholar 

  29. Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393(6684), 440–442 (1998)

    Article  Google Scholar 

Download references

Acknowledgement

This work was partially supported by the Ministry of Science and Technology (Taiwan) Grant Numbers 107-2118-M-001-011-MY3 and 108-2321-B-001-016.

Author information

Authors and Affiliations

  1. National Center for High-Performance Computing, Hsinchu, Taiwan

    Tai-Chi Wang

  2. Academia Sinica, Taipei, Taiwan

    Frederick Kin Hing Phoa

Authors
  1. Tai-Chi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frederick Kin Hing Phoa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Frederick Kin Hing Phoa .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito

  3. University of Aveiro, Aveiro, Portugal

    José Fernendo Mendes

  4. Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, TC., Phoa, F.K.H. (2020). A Generalized Framework for Detecting Social Network Communities by the Scanning Method. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_21

Download citation

Publish with us

Policies and ethics

Search

Navigation