Abstract
Algorithms for detecting communities in complex networks are generally unsupervised, relying solely on the structure of the network. However, these methods can often fail to uncover meaningful groupings that reflect the underlying communities in the data, particularly when those structures are highly overlapping. One way to improve the usefulness of these algorithms is by incorporating additional background information, which can be used as a source of constraints to direct the community detection process. In this work, we explore the potential of semi-supervised strategies to improve algorithms for finding overlapping communities in networks. Specifically, we propose a new method, based on label propagation, for finding communities using a limited number of pairwise constraints. Evaluations on synthetic and real-world datasets demonstrate the potential of this approach for uncovering meaningful community structures in cases where each node can potentially belong to more than one community.
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References
Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466(7307), 761–764 (2010)
Amelio, A., Pizzuti, C.: Overlapping community discovery methods: a survey. Social Networks: Analysis and Case Studies, pp. 105–125. Springer, Berlin (2014)
Blondel, V., Guillaume, J., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. 10008, P10008 (2008)
Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066,111 (2004)
Dreier, J., Kuinke, P., Przybylski, R., Reidl, F., Rossmanith, P., Sikdar, S.: Overlapping communities in social networks. arXiv preprint arXiv:1412.4973 (2014)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)
Girvan, M., Newman, M.E.: Community structure in social and biological networks. PNAS 99(12), 7821–7826 (2002)
Gregory, S.: Finding overlapping communities in networks by label propagation. New J. Phys. 12(10), 103,018 (2010)
Habashi, S., Ghanem, N.M., Ismail, M.A.: Enhanced community detection in social networks using active spectral clustering. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, pp. 1178–1181 (2016)
Harenberg, S., et al.: Community detection in large-scale networks: a survey and empirical evaluation. Wiley Interdiscip. Rev. Comput. Stat. 6(6), 426–439 (2014)
Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. 11(3), 033,015 (2009)
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046,110 (2008)
Lancichinetti, A., Radicchi, F., Ramasco, J., Fortunato, S., Ben-Jacob, E.: Finding statistically significant communities in networks. PLoS ONE 6(4), e18,961 (2011)
Lee, C., Reid, F., McDaid, A., Hurley, N.: Detecting highly overlapping community structure by greedy clique expansion. In: Workshop on Social Network Mining and Analysis (2010)
Leng, M., Yao, Y., Cheng, J., Lv, W., Chen, X.: Active semi-supervised community detection algorithm with label propagation. In: International Conference on Database Systems for Advanced Applications, pp. 324–338. Springer, Berlin (2013)
Leskovec, J., Krevl, A.: SNAP datasets: stanford – large network dataset collection (2015)
Li, L., Du, M., Liu, G., Hu, X., Wu, G.: Extremal optimization-based semi-supervised algorithm with conflict pairwise constraints for community detection. In: Proceedings of the ASONAM2014, pp. 180–187 (2014)
Liu, D., Duan, D., Sui, S., Song, G.: Effective semi-supervised community detection using negative information. Math. Probl. Eng. 2015, 8 (2015)
McDaid, A., Hurley, N.: Detecting highly overlapping communities with model-based overlapping seed expansion. In: Proceedings of the ASONAM2010, pp. 112–119 (2010)
Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)
Xie, J., Szymanski, B.K., Liu, X.: SLPA: uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In: Proceedings of the IEEE 11th International Conference on Data Mining Workshops, pp. 344–349 (2011)
Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. Knowl. Inf. Syst. 42(1), 181–213 (2015)
Zhang, Z.Y.: Community structure detection in complex networks with partial background information. EPL (Europhys. Lett.) 101(4), 48,005 (2013)
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Alghamdi, E., Greene, D. (2019). Semi-supervised Overlapping Community Finding Based on Label Propagation with Pairwise Constraints. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham. https://doi.org/10.1007/978-3-030-05411-3_26
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DOI: https://doi.org/10.1007/978-3-030-05411-3_26
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