Abstract
This paper introduces a multi-objective optimization idea to solve the community detection. First, the problem of community detection is transformed into complex multi-objective optimization problem. Second, an evolutionary multi-objective membrane algorithm is proposed for discovering community structure. Finally, the proposed algorithm is conducted on the synthetic networks, and the experimental results demonstrate that our algorithm is effective and promising, and it can detect communities more accurately compared with PSO and GSA.
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
Similar content being viewed by others
References
Gong, M., Fu, B., Jiao, L., Du, H.: Memetic algorithm for community detection in networks. Phys. Rev. E 84(5), 056101 (2011)
Cai, Q., Ma, L., Gong, M., Tian, D.: A survey on network community detection based on evolutionary computation. Int. J. Bio-inspired Comput. 8(2), 84–98 (2016)
Atay, Y., Koc, I., Babaoglu, I., Kodaz, H.: Community detection from biological and social networks: a comparative analysis of metaheuristic algorithms. Appl. Soft Comput. 50, 194–211 (2017)
Pizzuti, C.: A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans. Evol. Comput. 16(3), 418–430 (2012)
Paun, G.: Computing with membranes. J. Comput. Syst. Sci. 61(1), 108–143 (2000)
Paun, G., Rozenberg, G.: A guide to membrane computing. Theoret. Comput. Sci. 287(1), 73–100 (2002)
Paun, G., Rozenberg, G., Salomaa, A.: The Oxford Handbook of Membrane Computing. Oxford University Press, Inc., Oxford (2010)
Ciobanu, G., Paun, G., Paerez-Jimaenez, M.J.: Applications of Membrane Computing. Springer, Heidelberg (2006). doi:10.1007/3-540-29937-8
Cecilia, J.M., Garcia, J.M., Guerrero, G.D., Martinez-del Amor, M.A., Perez-Jimenez, M.J., Ujaldon, M.: The GPU on the simulation of cellular computing models. Soft. Comput. 16(2), 231–246 (2012)
Pan, L., Martin-Vide, C.: Solving multidimensional 0–1 knapsack problem by P systems with input and active membranes. J. Parallel Distrib. Comput. 65(12), 1578–1584 (2005)
Liu, C., Chen, D., Wan, F.: Multiobjective learning algorithm based on membrane systems for optimizing the parameters of extreme learning machine. Optik - Int. J. Light Electron Opt. 127(4), 1909–1917 (2015)
Singh, G., Deep, K., Nagar, A.K.: Cell-like P-systems based on rules of particle swarm optimization. Appl. Math. Comput. 246, 546–560 (2014)
Nishida, T.Y.: An approximate algorithm for NP-complete optimization problems exploiting P systems. In: Proceedings of Brainstorming Workshop on Uncertainty in Membrane Computing, pp. 185–192 (2004)
Zhang, Y., Huang, L.: A variant of P systems for optimization. Neurocomputing 72(4), 1355–1360 (2009)
Zhang, G., Gheorghe, M., Wu, C.: A quantum-inspired evolutionary algorithm based on P systems for knapsack problem. Fundam. Inform. 87(1), 93–116 (2008)
Huang, L., Suh, I.H., Abraham, A.: Dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants. Inf. Sci. 181(11), 2370–2391 (2011)
Buiu, C., Vasile, C., Arsene, O.: Development of membrane controllers for mobile robots. Inf. Sci. 187, 33–51 (2012)
Liu, C., Han, M., Wang, X.: A multi-objective evolutionary algorithm based on membrane systems. In: 2011 Fourth International Workshop on Advanced Computational Intelligence (IWACI), pp. 103–109. IEEE (2011)
Zhang, G., Rong, H., Neri, F., Perez-Jimenez, M.J.: An optimization spiking neural P system for approximately solving combinatorial optimization problems. Int. J. Neural Syst. 24(05), 1440006 (2014)
Liu, C., Fan, L.: Evolutionary algorithm based on dynamical structure of membrane systems in uncertain environments. Int. J. Biomath. 9(02), 1650017 (2016)
Xiao, J., He, J.J., Chen, P., Niu, Y.Y.: An improved dynamic membrane evolutionary algorithm for constrained engineering design problems. Natural Comput. 1–11 (2016)
Liu, C., Fan, L.: A hybrid evolutionary algorithm based on tissue membrane systems and CMA-ES for solving numerical optimization problems. Knowl.-Based Syst. 105, 38–47 (2016)
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)
Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)
Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Nat. Acad. Sci. 99(12), 7821–7826 (2002)
Acknowledgments
This project was supported by Shenyang Science and Technology Program (Grant No. 17-175-3-00).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Liu, C., Fan, L., Li, L., Liu, Z., Dai, X., Gao, W. (2017). Community Detection in Networks by Using Multiobjective Membrane Algorithm. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-70093-9_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70092-2
Online ISBN: 978-3-319-70093-9
eBook Packages: Computer ScienceComputer Science (R0)