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A Comparison of Community Detection Algorithms on Artificial Networks

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Discovery Science (DS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5808))

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Abstract

Community detection has become a very important part in complex networks analysis. Authors traditionally test their algorithms on a few real or artificial networks. Testing on real networks is necessary, but also limited: the considered real networks are usually small, the actual underlying communities are generally not defined objectively, and it is not possible to control their properties. Generating artificial networks makes it possible to overcome these limitations. Until recently though, most works used variations of the classic Erdős-Rényi random model and consequently suffered from the same flaws, generating networks not realistic enough. In this work, we use Lancichinetti et al. model, which is able to generate networks with controlled power-law degree and community distributions, to test some community detection algorithms. We analyze the properties of the generated networks and use the normalized mutual information measure to assess the quality of the results and compare the considered algorithms.

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References

  1. Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45, 167–256 (2003)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  3. Duch, J., Arenas, A.: Community detection in complex networks using extremal optimization. Physical Review E 72, 027104 (2005)

    Article  Google Scholar 

  4. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America 99, 7821–7826 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  5. Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)

    Article  Google Scholar 

  6. Reichardt, J., Bornholdt, S.: Detecting Fuzzy Community Structures in Complex Networks with a Potts Model. Physical Review Letters 93, 218701 (2004)

    Article  Google Scholar 

  7. Falkowski, T., Barth, A., Spiliopoulou, M.: DENGRAPH: A Density-based Community Detection Algorithm. In: IEEE/WIC/ACM International Conference on Web Intelligence, pp. 112–115 (2007)

    Google Scholar 

  8. Liu, Y., Wang, Q., Wang, Q., Yao, Q., Liu, Y.: Email Community Detection Using Artificial Ant Colony Clustering. In: Chang, K.C.-C., Wang, W., Chen, L., Ellis, C.A., Hsu, C.-H., Tsoi, A.C., Wang, H. (eds.) APWeb/WAIM 2007. LNCS, vol. 4537, pp. 287–298. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  9. Hoff, P., Raftery, A., Handcock, M.: Latent space approaches to social network analysis. Journal of the American Statistical Association 97, 1090–1098 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  10. Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Physical Review E 69, 066133 (2004)

    Article  Google Scholar 

  11. Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007)

    Article  Google Scholar 

  12. Erdos, P., Renyi, A.: On random graphs. Publicationes Mathematicae 6, 290–297 (1959)

    MathSciNet  MATH  Google Scholar 

  13. Danon, L., Diaz-Guilera, A., Arenas, A.: The effect of size heterogeneity on community identification in complex networks. Journal of Statistical Mechanics-Theory and Experiment 11010 (2006)

    Google Scholar 

  14. Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E Stat. Nonlin Soft. Matter Phys. 78, 46110 (2008)

    Article  Google Scholar 

  15. Kuncheva, L.I., Hadjitodorov, S.T.: Using diversity in cluster ensembles. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 1214–1219 (2004)

    Google Scholar 

  16. Fred, A.L.N., Jain, A.K.: Robust Data Clustering. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 128–136. IEEE Computer Society, Los Alamitos (2003)

    Google Scholar 

  17. Danon, L., Díaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. J. Stat. Mech P09008 (2005)

    Google Scholar 

  18. Holme, P., Kim, B.J., Yoon, C.N., Han, S.K.: Attack vulnerability of complex networks. Physical Review E 65, 026139 (2002)

    Article  Google Scholar 

  19. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.: Complex networks: structure and dynamics. Physics Reports 424, 175–308 (2006)

    Article  MathSciNet  Google Scholar 

  20. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network Motifs: Simple Building Blocks of Complex Networks. Science 298, 824–827 (2002)

    Article  Google Scholar 

  21. Watts, D., Strogatz, S.H.: Collective dynamics of ’small-world’ networks. Nature 393, 409–410 (1998)

    Article  Google Scholar 

  22. Barabasi, A., Albert, R.: Statistical mechanics of complex networks. Reviews of Modern physics 74, 47–96 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  23. Guimerà, R., Danon, L., Díaz-Guilera, A., Giralt, F., Arenas, A.: Self-similar community structure in a network of human interactions. Physical Review E 68, 065103 (2003)

    Article  Google Scholar 

  24. Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Physical Review E 70, 066111 (2004)

    Article  Google Scholar 

  25. Cohen, J.: A Coefficient of Agreement for Nominal Scales. Educational Psychology Measurement 20, 37–46 (1960)

    Article  Google Scholar 

  26. Fortunato, S., Barthelemy, M.: Resolution limit in community detection. Proceedings of the National Academy of Science of the USA 104, 36–41 (2007)

    Article  Google Scholar 

  27. Guimerà, R., Sales-Pardo, M., Amaral, L.A.N.: Modularity from fluctuations in random graphs and complex networks. Physical Review E 70, 025101 (2004)

    Article  Google Scholar 

  28. Derenyi, I., Palla, G., Vicsek, T.: Clique percolation in random networks. Physical Review Letters 94 (2005)

    Google Scholar 

  29. Pons, P., Latapy, M.: Computing communities in large networks using random walks. In: Yolum, p., Güngör, T., Gürgen, F., Özturan, C. (eds.) ISCIS 2005. LNCS, vol. 3733, pp. 284–293. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  30. Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, 036104 (2006)

    Article  MathSciNet  Google Scholar 

  31. Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Phys. Rev. E 74, 016110 (2006)

    Article  MathSciNet  Google Scholar 

  32. Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of the United States of America 101, 2658–2663 (2004)

    Article  Google Scholar 

  33. Donetti, L., Munoz, M.A.: Detecting network communities: a new systematic and efficient algorithm. Journal of Statistical Mechanics: Theory and Experiment P10012 (2004)

    Google Scholar 

  34. Molloy, M., Reed, B.: A critical point for random graphs with a given degree sequence. Random Structures and Algorithms 6, 161–179 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  35. Serrano, M., Boguñá, M.: Weighted Configuration Model. In: AIP Conference Proceedings, vol. 776, p. 101 (2005)

    Google Scholar 

  36. Chung, F., Lu, L.: The average distances in random graphs with given expected degrees. PNAS 99, 15879–15882 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  37. Barabási, A.-L., Albert, R.: Emergence of Scaling in Random Networks. Science 286, 509–512 (1999)

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science Department, Galatasaray University, Ortaköy/İstanbul, Turkey

    Günce Keziban Orman & Vincent Labatut

  2. TÜBİTAK, Gebze/Kocaeli, Turkey

    Günce Keziban Orman

Authors
  1. Günce Keziban Orman
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  2. Vincent Labatut
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Editor information

Editors and Affiliations

  1. Faculty of Economics; Rua Dr. Roberto Frias, University of Porto, 4200-465, Porto, Portugal

    João Gama

  2. DCC-FC, Universidade do Porto, Portugal

    Vítor Santos Costa

  3. LIACC/FEP, Universidade do Porto, Portugal

    Alípio Mário Jorge

  4. LIAAD-INESC Porto L.A./Faculty of Economics, University of Porto, Rua de Ceuta, 118-6, 4050-190, Porto, Portugal

    Pavel B. Brazdil

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© 2009 Springer-Verlag Berlin Heidelberg

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Orman, G.K., Labatut, V. (2009). A Comparison of Community Detection Algorithms on Artificial Networks. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds) Discovery Science. DS 2009. Lecture Notes in Computer Science(), vol 5808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04747-3_20

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  • DOI: https://doi.org/10.1007/978-3-642-04747-3_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04746-6

  • Online ISBN: 978-3-642-04747-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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