Abstract
Community structure is an important feature of complex networks and it is essential for analyzing complex networks. In recent years, community detection based on heuristic algorithms has received much attention in various fields. To improve the effectiveness and accuracy of the algorithm in big data era, an adaptive brain storm optimization based on hierarchical learning (ABSO-HL) is proposed. Instead of the fixed probability, the adaptive probability is adopted in mutation and crossover operations of the proposed ABSO-HL. The proposed updated strategy selects one or two solutions for mutation and crossover to generate a new one. The proposed hierarchical learning strategy is used to accelerate the process by searching in the neighborhood of the new solution, and obtain the optimal partition in an efficient way. The usefulness and effectiveness of the proposed algorithm were demonstrated through a lot of experiments on both real-world and synthetic networks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Strogatz, S.: Exploring complex networks. Nature 410, 268–276 (2001)
Latora, V., Nicosia, V., Russo, G.: Complex Networks: Principles, Methods and Applications, 1st edn. Cambridge University Press, UK (2017)
Lyu, C., Shi, Y., Sun, L.: A Novel Local Community Detection Method Using Evolutionary Computation. IEEE Trans. Cybern. 51(6), 3348–3360 (2021)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Nat. Acad. Sci. 99(12), 7821–7826 (2002)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)
Zeng, X., Wang, W., Chen, C., Yen, G.G.: A consensus community-based particle swarm optimization for dynamic community detection. IEEE Trans. Cybern. 50(6), 2502–2513 (2020)
Lu, M., Zhang, Z., Qu, Z., Kang, Y.: LPANNI: Overlapping community detection using label propagation in large-scale complex networks. IEEE Trans. Knowl. Data Eng. 31(9), 1736–1749 (2019)
Yang, L., Cao, X., He, D., Wang, C. , Zhang, W.: Modularity based community detection wih deep learning. In: Proceedings of International Joint Conference on Artificial Intelligence, pp. 2252–2258 (2016)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)
Zhan, Z., Shi, L., Tan, K., Zhang, J.: A survey on evolutionary computation for complex continuous optimization. Artificial Intell. Rev. 55, 59–110 (2021)
Guimerà, R., Sales-Pardo, M., Amaral, L.A.N.: Modularity from fluctuations in random graphs and complex networks. Phys. Rev. E 70(2), 025101 (2004)
Duch, J., Arenas, A.: Community detection in complex networks using extremal optimization. Phys. Rev. E 72(2), 027104 (2005)
Liang, S., Li, H., Gong, M., Wu, Y., Zhu, Y.: Distributed multi-objective community detection in large-scale and complex networks. In: Proceedings of the 2019 15th International Conference on Computational Intelligence and Security (CIS), pp. 201–205 (2019)
Bian, K., Sun, Y., Cheng, S., Liu, Z., Sun, X.: Adaptive methods of differential evolution multi-objective optimization algorithm based on decomposition. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds.) NCAA 2021. CCIS, vol. 1449, pp. 458–472. Springer, Singapore (2021)
Chakraborty, T., Ghosh, S., Park, N.: Ensemble-based overlapping community detection using disjoint community structures. Knowl.-Based. Syst 163, 241–251 (2019)
Shi, Y.: Brain storm optimization algorithm. In: International Conference on Swarm Intelligence, vol. Part I, pp. 303–309 (2011)
Estévez, P.A., Tesmer, M., Perez, C.A., et al.: Normalized mutual information feature selection. IEEE Trans. Neural Networks 20(2), 189–201 (2009)
Danon, L., Díaz-Guilera, A., Duch, J., et al.: Comparing community structure identification. J. Stat. Mech: Theory Exp. 2005(09), P09008 (2005)
Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035 (2007)
Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. U.S.A. 103(23), 8577–8582 (2006)
Guimerà, R., Danon, L., Díaz-Guilera, A.: Self-similar community structure in a network of human interactions. Phys. Rev. E 68(6), 065103 (2003)
Gleiser, P., Danon, L.: Community structure in jazz. Adv. Complex Syst. 6(4), 565 (2003)
Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav. Ecol. Sociobiol. 54(4), 396–405 (2003)
Zachary, W.W.: An information-flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)
Gong, M., Cai, Q., Chen, X., Ma, L.: Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition. IEEE Trans. Evol. Comput. 18(1), 82–97 (2014)
Gong, M., Fu, B., Jiao, L., Du, H.: Memetic algorithm for community detection in networks. Phys. Rev. E 84(5), 056101 (2011)
Pizzuti, C.: GA-Net: a genetic algorithm for community detection in social networks. Proc. Parallel Problem Solving Nat. 5199, 1081–1090 (2008)
Gong, M., Ma, L., Zhang, Q., Jiao, L.: Community detection in networks by using multiobjective evolutionary algorithm with decomposition. Phys. A 391(15), 4050–4060 (2012)
Shi, C., Yan, Z., Cai, Y., Wu, B.: Multi-objective community detection in complex networks. Appl. Soft Comput. 12(2), 850–859 (2012)
Pizzuti, C.: A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans. Evol. Comput. 16(3), 418–430 (2012)
Coello, C., Pulido, G., Lechuga, M.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 256–279 (2004)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)
Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. USA 105(4), 1118–1123 (2008)
Acknowledgements
This work was supported by the Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2022JM-381,2017JQ6070) National Natural Science Foundation of China (Grant No. 61703256), Foundation of State Key Laboratory of Public Big Data (No. PBD2022–08) and the Fundamental Research Funds for the Central Universities (Program No.GK202201014, GK202202003, GK201803020).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shi, W. et al. (2023). An Adaptive Brain Storm Optimization Based on Hierarchical Learning for Community Detection. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1869. Springer, Singapore. https://doi.org/10.1007/978-981-99-5844-3_28
Download citation
DOI: https://doi.org/10.1007/978-981-99-5844-3_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-5843-6
Online ISBN: 978-981-99-5844-3
eBook Packages: Computer ScienceComputer Science (R0)