Abstract
In this paper, we propose a method for detecting communities from signed social networks with both positive and negative weights by modeling the problem as a multi-objective problem. In the experiments, both real world and synthetic signed networks whose size ranges from 100 to 1200 nodes are used to validate the performance of the new algorithm. A comparison is also made between the new algorithm and an effective existing algorithm, namely FEC. The experimental results show that our algorithm obtains a good performance on both real world and synthetic data, and outperforms FEC clearly.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)
Doreian, P., Mrvar, A.: A partitioning approach to structural balance. Social Networks 18(2), 149–168 (1996)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E. 70(6) (2004)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(7) (2008)
Tasgin, M., Herdagdelen, A., Bingol, H.: Community detection in complex networks using genetic algorithms. arXiv, 2007(11) (2007)
Pizzuti, C.: GA-Net: A genetic algorithm for community detection in social networks. Parallel Problem Solving from Nature 5199, 1081–1090 (2008)
Yang, B., Cheung, W.K., Liu, J.: Community mining from signed social networks. IEEE Trans. Knowl. Data Eng. 19(10), 1333–1348 (2007)
Zitzler, E., Thiele, L.: Multi-objective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. on Evolutionary Computation 3(4), 257–271 (1999)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Vol. TIK-Report, No. 103 (2002)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6(26), 182–197 (2002)
Goh, C.K., Tan, K.C.: An investigation on noisy environments in evolutionary multiobjective optimization. IEEE Trans. on Evolutionary Computation 11(3), 354–381 (2007)
Pizzuti, C.: A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans. Evolutionary Computation 16(3), 418–430 (2012)
Wu, L., Ying, X., Wu, X., Lu, A., Zhou, Z.: Examining spectral space of complex network with positive and negative links. International Journal of Social Network Mining 1(1), 91–111 (2012)
Li, Y., Liu, J., Liu, C.: A comparative analysis of evolutionary and memetic algorithms for community detection from signed social networks. Soft Computing 18(2), 329–348 (2014)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2) (2004)
Gòmez, S., Jensen, P., Arenas, A.: Analysis of community structure in networks of correlated data. Phys. Rev. E. 80(1) (2009)
He, D.X.: Research on intelligent algorithms for network community mining, Master’s thesis, Jilin University, China (2010)
Jin, D., Liu, J., Yang, B., He, D.X., Liu, D.Y.: A genetic algorithm with local search strategy for improved detection of community structure. Acta Automatica Sinica 37(7) (2011)
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E. 76(3), 53–60 (2007)
Hruschka, E.R., Campello, R.J.G.B., Freitas, A.A., de Carvalho, A.C.P.L.F.: A survey of evolutionary algorithms for clustering. IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews 39(2), 133–155 (2009)
Shi, C., Yan, Z., Wang, Y., Cai, Y., Wu, B.: Genetic algorithm with local search for community detection in large-scale complex networks. Advances in Complex Systems 13(1), 873–882 (2010)
Danon, L., Diaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. J. Stat. Mech. Theory Exp. 2005(9) (2005)
Kropivnik, S., Mrvar, A.: An analysis of the Slovene Parliamentary Parties network. Developments in Statistics and Methodology, pp. 149–168 (1996)
Read, K.E.: Cultures of the central highlands, New Guinea. Southwestern J. Anthropology 10(1), 1–43 (1954)
Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Liu, C., Liu, J., Jiang, Z.: A multi-objective evolutionary algorithm based on similarity for community detection from signed social networks. IEEE Trans. on Cybernetics (2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zeng, Y., Liu, J. (2015). Community Detection from Signed Social Networks Using a Multi-objective Evolutionary Algorithm. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, K. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-13359-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-13359-1_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13358-4
Online ISBN: 978-3-319-13359-1
eBook Packages: EngineeringEngineering (R0)