Abstract
Community detection is an important method to reveal the characteristics of complex systems, which usually requires the system to meet the conditions of close connections within communities but sparse connections between communities. In view of this, community detection has been proven to be an NP-hard problem. Multi-objective evolutionary algorithm (MOEA) is an indispensable aspect of multi-layer network community detection. However, most MOEA-based multi-layer network detection algorithms only take the acquired prior information as the network preprocessing method and ignore its full utilization in optimization, resulting in the accuracy of network partition cannot be guaranteed. To this end, this paper proposes a multi-objective community detection algorithm based on multi-layer network reduction (MOEA-MR). Specifically, we use the non-negative matrix factorization method to generate the consistent prior information layer of multi-layer network. Based on this, a network reduction strategy based on node degree is constructed to recursively reduce the size of the prior information network. In addition, in the evolution process, we consider using the multi-layer network similarity to correct the mis-divided nodes in the local reduction community. Compared with other advanced community detection algorithms, the experimental results on the real-world and synthetic multi-layer networks proved the superiority of MOEA-MR.
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Gao, C., Su, Z., Liu, J., Kurths, J.: Even central users do not always drive information diffusion. Commun. ACM 62(2), 61–67 (2019)
Gao, C., Fan, Y., Jiang, S., Deng, Y., Liu, J., Li, X.: Dynamic robustness analysis of a two-layer rail transit network model. IEEE Trans. Intel. Trans. Sys. (2021). https://doi.org/10.1109/TITS.2021.3058185
Ma, X., Dong, D.: Community detection in multi-layer networks using joint nonnegative matrix factorization. IEEE Trans. Knowl. Data Eng. 31(2), 273–286 (2019)
Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)
Blondel, V., Guillaume, J., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. 8, P10008 (2008)
Shi, C., Yan, Z., Wang, Y.: A genetic algorithm for detecting communities in large-scale complex networks. Adv. Complex Syst. 13(1), 3–17 (2010)
Pizzuti, C.: GA-Net: a genetic algorithm for community detection in social networks. In: The Proceedings of 10th International Conference on PPSN, pp. 1081–1090 (2008)
Pizzuti, C.: A multi-objective genetic algorithm for community detection in networks. In: The 2009 IEEE International Conference on Tools Artificial Intelligence, pp. 379–386 (2009)
Shi, C., Yan, Z., Cai, Y.: Multi-objective community detection in complex networks. Appl. Soft Comput. 12(2), 850–859 (2012)
Pizzuti, C.: Multiobjective optimization and local merge for clustering attributed graphs. IEEE Trans. Cyber. 50(12), 4997–5009 (2020)
Li, X., Qi, X., Liu, X.: A discrete moth-flame optimization with an \(l_2\)-norm constraint for network clustering. IEEE Trans. Net. Sci. Eng. 9(3), 1776–1788 (2022)
Yang, L., Cao, X.: A unified semi-supervised community detection framework using latent space graph regularization. IEEE Trans. Cybern. 45(11), 2585–2598 (2015)
Gligorijevic, V., Zafeiriou, S.: Non-negative matrix factorizations for multiplex network analysis. IEEE Trans. Pattern Anal. Mach. Intell. 41(4), 928–940 (2019)
Xie, Y., Gong, M., Wang, S., Yu, B.: Community discovery in networks with deep sparse filtering. Pattern Recogn. 81, 50–59 (2018)
Zhang, Q., Li, H.: MOEA/D: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
Bródka, P.: A method for group extraction and analysis in multilayer social networks. CoRR abs/1612.02377 (2016)
Liu, W., Wang, S.: An improved multiobjective evolutionary approach for community detection in multilayer networks. In: The 2017 IEEE Congress on Evolutionary Computation, Donostia, pp. 443–449 (2017)
Ni, J., Cheng, W., Fan, W., Zhang, X.: ComClus: a self-grouping framework for multi-network clustering. IEEE Trans. Knowl. Data Eng. 30(3), 435–448 (2018)
Wang, H., Yang, Y., Liu, B.: GMC: graph-based multi-view clustering. IEEE Trans. Knowl. Data Eng. 32(6), 1116–1129 (2020)
Acknowledgements
This work was supported by the National Key R&D Program of China (2019YFB2102300), National Natural Science Foundation of China (61976181, 11931015), Natural Science Basic Research Plan in Shaanxi Province of China (2022JM-325) and Fundamental Research Funds for the Central Universities (D5000210738).
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Qi, X., He, L., Wang, J., Du, Z., Luo, Z., Li, X. (2022). A Multi-objective Evolutionary Algorithm Based on Multi-layer Network Reduction for Community Detection. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham. https://doi.org/10.1007/978-3-031-10989-8_12
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DOI: https://doi.org/10.1007/978-3-031-10989-8_12
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