Abstract
A community is typically viewed as a group of nodes, and most connections in the community generally happen between interior nodes. Community in network also overlap as a person may belong to more than one social group. Therefore, detecting overlapping partition of a network is necessary for the realistic social analysis. In this paper, We develop a fuzzy evaluation using the membership degree of each node belonging to every community, and present a fuzzy evaluation based memetic algorithm for overlapping community detection in network. Our proposed algorithm is a synergy of genetic algorithm with a variant of fuzzy K-means strategy as the local search procedure. Experiments in real-world networks show that our method has an excellent performance in identifying overlapping structures.
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 subscriptionsReferences
Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2005)
Guimer, R., Salespardo, M., Amaral, L.A.: Modularity from fluctuations in random graphs and complex networks. Phys. Rev. E 70(70), 188–206 (2004)
Duch, J., Arenas, A.: Community detection in complex networks using extremal optimization. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 72(2), 986–1023 (2005)
Fortunato, S., Barthelemy, M.: Resolution limit in community detection. Proc. Natl. Acad. Sci. 104(1), 36–41 (2007)
Li, Z., Zhang, S., Wang, R.S., Zhang, X.S., Chen, L.: Quantitative function for community detection. Phys. Rev. E 77(3), 036109 (2008)
Lee, C., Reid, F., McDaid, A., Hurley, N.: Detecting highly overlapping community structure by greedy clique expansion. arXiv preprint arXiv:1002.1827 (2010)
Farkas, I., Ábel, D., Palla, G., Vicsek, T.: Weighted network modules. New J. Phys. 9(6), 180 (2007)
Gregory, S.: An algorithm to find overlapping community structure in networks. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 91–102. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74976-9_12
Gong, M., Fu, B., Jiao, L., Du, H.: Memetic algorithm for community detection in networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 84(2), 4146–4152 (2011)
Gong, M., Cai, Q., Li, Y., Ma, J.: An improved memetic algorithm for community detection in complex networks. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2012)
Tibly, G., Kertsz, J.: On the equivalence of the label propagation method of community detection and a Potts model approach. Phys. A Stat. Mech. Appl. 387(19–20), 4982–4984 (2008)
Buzna, L., Lozano, S., Dazguilera, A.: Synchronization in symmetric bipolar population networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 80(6), 869–875 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhao, W., Guo, Y., Lei, C., Yan, J. (2016). Overlapping Community Detection in Network: A Fuzzy Evaluation Approach. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_5
Download citation
DOI: https://doi.org/10.1007/978-981-10-3614-9_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3613-2
Online ISBN: 978-981-10-3614-9
eBook Packages: Computer ScienceComputer Science (R0)