Abstract
Detecting communities is of great importance in the study of complex networks. In this study, the community detection problem is formulated as a multiobjective optimization problem; then a local search-based multiobjective optimization algorithm is proposed. In the proposed algorithm, different objectivewise local searches are designed for different objectives. These simple but effective local searches cooperate to simultaneously optimize two objectives. Extensive experiments on both synthetic and real-world networks show that the proposed algorithm obtains better or competitive results compared with existing state-of-the-art algorithms.
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Angelini L, Boccaletti S, Marinazzo D, Pellicoro M, Stramaglia S (2007) Identification of network modules by optimization of ratio association. Chaos 17(2):023114
Blondel V, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech: Theory Exp 2008(10):P10008
Cai Q, Gong M, Shen B, Ma L, Jiao L (2014a) Discrete particle swarm optimization for identifying community structures in signed social networks. Neural Networks 58:4–13
Cai Q, Gong M, Ma L, Ruan S, Yuan F, Jiao L (2015) Greedy discrete particle swarm optimization for large-scale social network clustering. Inf Sci. doi:10.1016/j.ins.2014.09.041
Cai Q, Gong M, Ma L, Jiao L (2014b) A novel clonal selection algorithm for community detection in complex networks. Comput Intell. doi:10.1111/coin.12031 (in Press)
Cai Q, Ma L, Gong M, Tian D (2014c) A survey on network community detection based on evolutionary computation. Int J Bio-Inspired Comput (in Press)
Carullo G, Castiglione A, De Santis A, Palmieri F (2015) A triadic closure and homophily-based recommendation system for online social networks. World Wide Web. doi:10.1007/s11280-015-0333-5 (in Press)
Castiglione A, Pizzolante R, De Santis A, Carpentieri B, Castiglione A, Palmieri F (2015) Cloud-based adaptive compression and secure management services for 3d healthcare data. Future Generation Comput Syst 43–44:120–134 (In Press)
Chaturvedi P, Dhara M, Arora D (2012) community detection in complex network via BGLL algorithm. Int J Comput Appl 48(1):32–42
Clauset A, Newman M, Moore C (2004) Finding community structure in very large networks. Phys Rev E-Stat Nonlinear Soft Matter Phys 70(2):066111
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182–197
Di Martino F, Sessa S (2013) A fuzzy particle swarm optimization algorithm and its application to hotspot events in spatial analysis. J Ambient Intell Humaniz Comput 4(1):85–97
Esposito C, Ficco M, Palmieri F, Castiglione A (2013) Interconnecting federated clouds by using publish-subscribe service. Clust Comput 16(4):887–903
Fortunato S, Barthelemy M (2007) Resolution limit in community detection. Proc Natl Acad Sci USA 104(1):36–41
Fortunato S (2010) Community detection in graphs. Phys Reports 486(3–5):75–174
Girvan M, Newman M (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99(12):7821–7826
Gong M, Ma L, Zhang Q, Jiao L (2012a) Community detection in networks by using multiobjective evolutionary algorithm with decomposition. Physica A: Stat. Mech Appl 391(15):4050–4060
Gong MG, Zhang LJ, Ma JJ, Jiao LC (2012b) Community detection in dynamic social networks based on multiobjective immune algorithm. J Comput Sci Technol 27(3):455–467
Gong M, Cai Q, Chen X, Ma L (2014) Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition. IEEE Trans Evol Comput 18(1):82–97
Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E Stat Nonlinear Soft Matter Phys 78(4):046110
Li J, Song Y (2013) Community detection in complex networks using extended compact genetic algorithm. Soft Comput 17(6):925–937
Li Y, Liu J, Liu C (2014) A comparative analysis of evolutionary and memetic algorithms for community detection from signed social networks. Soft Comput 18(2):329–348
Liu C, Liu J, Jiang Z (2014) A multiobjective evolutionary algorithm based on similarity for community detection from signed social networks. IEEE Trans Cybern 44(12):2274–2287
Li J, Wang Q, Wang C, Cao N, Ren K, Lou W (2010) Fuzzy keyword search over encrypted data in cloud computing. In: Proceedings of the 29th IEEE International Conference on Computer Communications, pp 441–445
Mokryani G, Siano P, Piccolo A (2013) Optimal allocation of wind turbines in microgrids by using genetic algorithm. J Ambient Intell Humaniz Comput 4(6):613–619
Newman M (2013) Network data sets. http://www-personal.umich.edu/~mejn/netdata/
Newman M (2004) Fast algorithm for detecting community structure in networks. Phys Rev E Stat Nonlinear Soft Matter Phys 69(2):066133
Pizzuti C (2008) GA-Net: a genetic algorithm for community detection in social networks. In: Lecture notes in computer science, vol 5199. LNCS, pp 1081–1090
Pizzuti C (2012) A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans Evol Comput 16(3):418–430
Radicchi F, Castellano C, Cecconi F, Loreto V, Paris D (2004) Defining and identifying communities in networks. Proc Natl Acad Sci USA 101(9):2658–2663
Raghavan U, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E Stat Nonlinear Soft Matter Phys 76(3):036106
Rubio-Largo A, Zhang Q, Vega-Rodriguez M (2014) Multiobjective evolutionary algorithm based on decomposition for 3-objective optimization problems with objectives in different scales. Soft Comput 19(1):157–166
Shi C, Yan Z, Cai Y, Wu B (2012) Multi-objective community detection in complex networks. Appl Soft Comput J 12(2):850–859
Srivastava V, Tripathi B, Pathak V (2014) Biometric recognition by hybridization of evolutionary fuzzy clustering with functional neural networks. J Ambient Intell Humaniz Comput 5(4):525–537
Talbi EG, Basseur M, Nebro A, Alba E (2012) Multi-objective optimization using metaheuristics: non-standard algorithms. Int Trans Oper Res 19(1–2):283–305
Tricoire F (2012) Multi-directional local search. Comput Oper Res 39(12):3089–3101
Wang J, Zhong C, Zhou Y, Zhou Y (2014 In press) Multiobjective optimization algorithm with objective-wise learning for continuous multiobjective problems. J Ambient Intell Humaniz Comput. (In Press)
Wang J, Zhou Y, Wang Y, Zhang J, Chen CP, Zheng Z (2015) Multiobjective vehicle routing problems with simultaneous delivery and pickup and time windows: Formulation, instances and algorithms. IEEE Trans Cybern. (In Press)
Wei YC, Cheng CK (1991) Ratio cut partitioning for hierarchical designs. IEEE Trans Comput-Aided Des Integr Circuits Syst 10(7):911–921
Wu F, Huberman B (2004) Finding communities in linear time: a physics approach. Eur Phys J B 38(2):331–338
Xie J, Kelley S, Szymanski B (2013) Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput Surv 45(4):1–36
Zhang Q, Member S, Li H (2007) MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731
Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state-of-the-art. Swarm Evol Comput 1(1):23–49
Zhou Y, Wang J (2014) A local search-based multiobjective optimization algorithm for multiobjective vehicle routing problem with time windows. IEEE Syst J. (In Press)
Acknowledgments
This work was supported in part by Zhujiang New Star Program of Science and Technology in Guangzhou (2012J2200085), Excellent Young Teachers Training Program in Guangdong Colleges and Universities (Yqgdufe1404), Program for Characteristic Innovation Talents of Guangdong (2014KTSCX127), and the National Natural Science Foundation of China (61472453, U1401256).
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The authors declare that they have no conflict of interest. This research does not involve any human participant or animal and thus has no informed consent.
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Communicated by V. Loia.
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Zhou, Y., Wang, J., Luo, N. et al. Multiobjective local search for community detection in networks. Soft Comput 20, 3273–3282 (2016). https://doi.org/10.1007/s00500-015-1706-5
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DOI: https://doi.org/10.1007/s00500-015-1706-5