Abstract
Community detection is one of the most important tasks in network analysis. Recently, an increasing number of researchers have been dedicated to investigating networks in which the nodes participate concomitantly in more than one community. This work presents a comparative study of five state-of-art methods for overlapping community detection from the perspective of the structural properties of the communities identified by them. Experiments with benchmark and ground-truth networks show that, although the methods are able to identify modular communities, they often miss many structural properties of the communities, such as the number of nodes in the overlapping region and the membership of the nodes.
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Notes
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Downloaded from: http://snap.stanford.edu/data/.
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Downloaded from: http://www-personal.umich.edu/~mejn/netdata/.
- 3.
The computational environment consists of an Intel Core i9-9900K processor with 32Gb RAM running an Ubuntu 18.04 OS.
References
Amelio, A., Pizzuti, C.: Overlapping community discovery methods: a survey. CoRR 1411.3935 (2014)
Coscia, M., Rossetti, G., Giannotti, F., Pedreschi, D.: Uncovering hierarchical and overlapping communities with a local-first approach. ACM Trans. Knowl. Discov. Data 9(1), 6:1–6:27 (2014)
Gregory, S.: Finding overlapping communities in networks by label propagation. New J. Phys. 12(10), 103018 (2010)
Hric, D., Darst, R.K., Fortunato, S.: Community detection in networks: structural communities versus ground truth. Phys. Rev. E 90, 062805 (2014)
Lancichinetti, A., Fortunato, S., Kertesz, J.: Detecting the overlapping and hierarchical community structure of complex networks. New J. Phys. 11, 033015 (2009)
Nicosia, V., Mangioni, G., Carchiolo, V., Malgeri, M.: Extending the definition of modularity to directed graphs with overlapping communities. J. Stat. Mech: Theory Exp. 2009(03), P03024 (2009)
Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814 (2005)
Raghavan, N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007)
Rhouma, D., Romdhane, L.B.: An efficient algorithm for community mining with overlap in social networks. Expert Syst. Appl. 41(9), 4309–4321 (2014)
Shen, H.W.: Detecting the overlapping and hierarchical community structure in networks, pp. 19–44. Springer, Heidelberg (2013)
Xie, J., Kelley, S., Szymanski, B.K.: Overlapping community detection in networks: the state of the art and comparative study. CoRR 1110.5813 (2011)
Yang, J., Leskovec, J.: Community-affiliation graph model for overlapping network community detection. In: Proceedings of the 2012 IEEE 12th International Conference on Data Mining, ICDM 2012, pp. 1170–1175. IEEE Computer Society, Washington, DC (2012)
Yang, J., Leskovec, J.: Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, WSDM 2013, pp. 587–596. ACM, New York (2013)
Zhou, L., Lü, K., Yang, P., Wang, L., Kong, B.: An approach for overlapping and hierarchical community detection in social networks based on coalition formation game theory. Expert Syst. Appl. 42(24), 9634–9646 (2015)
Acknowledgement
The authors would like to thank the Brazilian research funding agencies CNPq and Capes for the support to this work.
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da F. Vieira, V., Xavier, C.R., Evsukoff, A.G. (2020). Comparing the Community Structure Identified by Overlapping Methods. 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_22
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DOI: https://doi.org/10.1007/978-3-030-36687-2_22
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