Abstract
Community detection is a very active field in complex networks analysis, consisting in identifying groups of nodes more densely interconnected relatively to the rest of the network. The existing algorithms are usually tested and compared on real-world and artificial networks, their performance being assessed through some partition similarity measure. However, artificial networks realism can be questioned, and the appropriateness of those measures is not obvious. In this study, we take advantage of recent advances concerning the characterization of community structures to tackle these questions. We first generate networks thanks to the most realistic model available to date. Their analysis reveals they display only some of the properties observed in real-world community structures. We then apply five community detection algorithms on these networks and find out the performance assessed quantitatively does not necessarily agree with a qualitative analysis of the identified communities. It therefore seems both approaches should be applied to perform a relevant comparison of the algorithms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
da Fontura Costa, L., Oliveira Jr., O.N., Travieso, G., Rodrigues, r.A., Villas Boas, P.R., Antiqueira, L., Viana, M.P., da Rocha, L.E.C.: Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications. arXiv physics.soc-ph, 0711.3199 (2008)
Fortunato, S.: Community Detection in Graphs. Phys. Rep. 486, 75–174 (2010)
Newman, M.E.J., Girvan, M.: Finding and Evaluating Community Structure in Networks. Phys. Rev. E 69, 26113 (2004)
Lancichinetti, A., Kivelä, M., Saramäki, J., Fortunato, S.: Characterizing the Community Structure of Complex Networks. PLoS ONE 5, e11976 (2010)
Girvan, M., Newman, M.E.J.: Community Structure in Social and Biological Networks. PNAS 99, 7821–7826 (2002)
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark Graphs for Testing Community Detection Algorithms. Phys. Rev. E 78, 46110 (2008)
Danon, L., Diaz-Guilera, A., Arenas, A.: The Effect of Size Heterogeneity on Community Identification in Complex Networks. J. Stat. Mech., 11010 (2006)
Newman, M.E.J.: The Structure and Function of Complex Networks. SIAM Rev. 45, 167–256 (2003)
Guimerà, R., Danon, L., Díaz-Guilera, A., Giralt, F., Arenas, A.: Self-Similar Community Structure in a Network of Human Interactions. Phys. Rev. E 68, 65103 (2003)
Orman, G.K., Labatut, V.: A comparison of community detection algorithms on artificial networks. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS, vol. 5808, pp. 242–256. Springer, Heidelberg (2009)
Orman, G.K., Labatut, V.: The Effect of Network Realism on Community Detection Algorithms. In: ASONAM, Odense, DK, pp. 301–305 (2010)
Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Statistical Properties of Community Structure in Large Social and Information Networks. In: WWW, ACM, Beijing (2008)
Guimerà, R., Amaral, L.A.N.: Functional Cartography of Complex Metabolic Networks. Nature 433, 895–900 (2005)
Newman, M.E.J.: Detecting Community Structure in Networks. Eur. Phys. J. B 38, 321–330 (2004)
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)
Erdõs, P., Rényi, A.: On Random Graphs. Publ. Math. 6, 290–297 (1959)
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)
Bagrow, J.P.: Evaluating Local Community Methods in Networks. J. Stat. Mech (2008)
Lancichinetti, A., Fortunato, S.: Community Detection Algorithms: A Comparative Analysis. Phys. Rev. E 80, 56117 (2009)
Molloy, M., Reed, B.: A Critical Point for Random Graphs with a Given Degree Sequence. Random Structures and Algorithms 6, 161–179 (1995)
Barabási, A.-L., Albert, R.: Emergence of Scaling in Random Networks. Science 286, 509 (1999)
Danon, L., Duch, J., Arenas, A., Díaz-Guilera, A.: Community Structure Identification. In: Large Scale Structure and Dynamics of Complex Networks: From Information Technology to Finance and Natural Science, pp. 93–113. World Scientific, Singapore (2007)
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast Unfolding of Communities in Large Networks. J. Stat. Mech., 10008 (2008)
Gan, G.a.M., C. and Wu, J.: Data Clustering: Theory, Algorithms, and Applications. Society for Industrial and Applied Mathematics, Philadelphia, US-PA (2007)
Rosvall, M., Bergstrom, C.T.: Maps of Random Walks on Complex Networks Reveal Community Structure. PNAS 105, 1118 (2008)
van Dongen, S.: Graph Clustering Via a Discrete Uncoupling Process. SIAM J. Matrix Anal. Appl. 30, 121–141 (2008)
Fortunato, S., Barthelemy, M.: Resolution Limit in Community Detection. PNAS 104, 36–41 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Orman, G.K., Labatut, V., Cherifi, H. (2011). Qualitative Comparison of Community Detection Algorithms. In: Cherifi, H., Zain, J.M., El-Qawasmeh, E. (eds) Digital Information and Communication Technology and Its Applications. DICTAP 2011. Communications in Computer and Information Science, vol 167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22027-2_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-22027-2_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22026-5
Online ISBN: 978-3-642-22027-2
eBook Packages: Computer ScienceComputer Science (R0)