Abstract
Evaluating a community detection algorithm is a complex task due to the lack of a shared and universally accepted definition of community. In literature, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i.e., Normalized Mutual Information). In this paper we propose a novel approach aimed at evaluating the adherence of a community partition to the ground truth: our methodology provides more information than the state-of-the-art ones and is fast to compute on large-scale networks. We evaluate its correctness by applying it to six popular community detection algorithms on four large-scale network datasets. Experimental results show how our approach allows to easily evaluate the obtained communities on the ground truth and to characterize the quality of community detection algorithms.
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- 1.
A Python implementation of our approach is available at: http://goo.gl/kWIH2I.
- 2.
The network datasets are available at: https://snap.stanford.edu/data/.
References
Fortunato, S.: Community detection in graphs, Physics Reports, vol. 486, no. 3–5, pp. 75–174 (2010). http://www.sciencedirect.com/science/article/B6TVP-4XPYXF1-1/2/99061fac6435db4343b2374d26e64ac1
Coscia, M., Giannotti, F., Pedreschi, D.: A classification for community discovery methods in complex networks. Stat. Anal. Data Min., 4(5), 512–546 (2011). http://dx.doi.org/10.1002/sam.10133
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E, 78(4), 046110 (2008). http://pre.aps.org/abstract/PRE/v78/i4/e046110
Bhat, S., Abulaish, M.: Overlapping social network communities and viral marketing. In: International Symposium on Computational and Business Intelligence, pp. 243–246, Aug 2013
Wu, X., Liu, Z.: How community structure influences epidemic spread in social networks. Phys. A: Stat. Mech. Appl. 387, 623–630 (2008)
Rossetti, G., Pappalardo, L., Kikas, R., Pedreschi, D., Giannotti, F., Dumas, M.: Community-centric analysis of user engagement in skype social network. In: Proceedings of the 2015 ACM/IEEE International Conference on Advances in Social Network Analysis and Mining (2015)
Rossetti, G., Guidotti, R., Pennacchioli, D., Pedreschi, D., Giannotti, D.: Interaction prediction in dynamic networks exploiting community discovery. In: Proceedings of the 2015 ACM/IEEE International Conference on Advances in Social Network Analysis and Mining (2015)
Rinzivillo, S., Mainardi, S., Pezzoni, F., Coscia, M., Giannotti, F., Pedreschi, D.: Discovering the geographical borders of human mobility. KI - Künstliche Intelligenz (2012)
Bagrow, J.P., Lin, Y.-R.: Mesoscopic structure and social aspects of human mobility. PLoS ONE 7(5), p. e37676, 2012. http://dx.doi.org/10.1371/journal.pone.0037676
Lancichinetti, A., Fortunato, S.: Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys. Rev. E 80(1), 016118 (2009)
McDaid, A.F., Greene, D., Hurley, N.J.: Normalized mutual information to evaluate overlapping community finding algorithms. CoRR, arXiv:1110.2515 (2011)
Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. (2009)
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008(10), P10008 (2008)
Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. National Acad. Sci. 105(4), 1118–1123 (2008)
Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)
Coscia, M., Rossetti, G., Giannotti, F., Pedreschi, D.: Demon: a local-first discovery method for overlapping communities. In: Agarwal, D., Pei, J. (eds.), KDD, Q. Y. 0001, pp. 615–623. ACM (2012)
Cazabet, R., Amblard, F., Hanachi, C.: Detection of overlapping communities in dynamical social networks. In: SocialCom, pp. 309–314 (2010)
Jaewon, Y., Leskovec, J.: Defining and evaluating network communities based on ground-truth. Knowl. Inf, Syst (2015)
Acknowledgments
This work was partially funded by the European Community’s H2020 Program under the funding scheme “FETPROACT-1-2014: Global Systems Science (GSS)”, grant agreement #641191 CIMPLEX “Bringing CItizens, Models and Data together in Participatory, Interactive SociaL EXploratories”, https://www.cimplex-project.eu. Our research is also supported by the European Community’s H2020 Program under the scheme “INFRAIA-1-2014-2015: Research Infrastructures”, grant agreement #654024 “SoBigData: Social Mining & Big Data Ecosystem”, http://www.sobigdata.eu.
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Rossetti, G., Pappalardo, L., Rinzivillo, S. (2016). A Novel Approach to Evaluate Community Detection Algorithms on Ground Truth. In: Cherifi, H., Gonçalves, B., Menezes, R., Sinatra, R. (eds) Complex Networks VII. Studies in Computational Intelligence, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-319-30569-1_10
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DOI: https://doi.org/10.1007/978-3-319-30569-1_10
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