RESEARCH ON DYNAMIC COMMUNITY DETECTION BASED ON A CONSENSUS COMMUNITY MULTIOBJECTIVE CUCKOO ALGORITHM
Keywords:
Cuckoo Algorithm, Community Detection, Consensus Community, Dynamic NetworkAbstract
Dynamic network community detection reveals patterns in community structure evolution over time and is therefore crucial for critical applications such as social network analysis. Such detection requires maximizing clustering accuracy at the current moment while minimizing results drift between two consecutive moments. The knowledge of the previous moment is the intra-community consensus extracted from the optimal community of the previous moment. Subsequently, the community of the current moment votes on the inter-community consensus of the previous moment during the evolutionary process. A subset of the internal-community consensus that receives high support will be considered the inter-community consensus of the earlier and current moments. The concept of inter-community consensus refers to the knowledge that can be transferred from one moment to another. The community at the current moment inserts inter-community consensus into the evolutionary process and can evolve in a direction similar to that of the community at the previous moment. The cuckoo algorithm guides the evolution process, which influences the community structure through evaluation, update, and mutation events. Experimental results on several artificial networks and real dynamic networks show that the method has higher accuracy and robustness than existing methods.
References
R. Couillet and Z. Liao, Random matrix methods for machine learning. Cambridge University Press, 2022.
G. Yao, J. Wang, B. Cui, and Y. Ma, "Quantifying effects of tasks on group performance in social learning," Humanities and Social Sciences Communications, vol. 9, no. 1, pp. 1-11, 2022.
N. Emirov, C. Cheng, Q. Sun, and Z. Qu, "Distributed algorithms to determine eigenvectors of matrices on spatially distributed networks," Signal Processing, vol. 196, p. 108530, 2022.
A. Bhattacharjee and M. S. Bayzid, "Machine learning based imputation techniques for estimating phylogenetic trees from incomplete distance matrices," BMC genomics, vol. 21, pp. 1-14, 2020.
B. Zarei and M. R. Meybodi, "Detecting community structure in complex networks using genetic algorithm based on object migrating automata," Computational Intelligence, vol. 36, no. 2, pp. 824-860, 2020.
H. Yang, B. Li, F. Cheng, P. Zhou, R. Cao, and L. Zhang, "A node classification-based multiobjective evolutionary algorithm for community detection in complex networks," IEEE Transactions on Computational Social Systems, 2022.
X. Zhou, X. Zhao, and Y. Liu, "A multiobjective discrete bat algorithm for community detection in dynamic networks," Applied Intelligence, vol. 48, pp. 3081-3093, 2018.
S. Srihari, C. H. Yong, and L. Wong, Computational prediction of protein complexes from protein interaction networks. Morgan & Claypool, 2017.
E. Akachar, B. Ouhbi, and B. Frikh, "ACSIMCD: A 2-phase framework for detecting meaningful communities in dynamic social networks," Future Generation Computer Systems, vol. 125, pp. 399-420, 2021.
R. A. Leite, A. Arleo, J. Sorger, T. Gschwandtner, and S. Miksch, "Hermes: guidance-enriched visual analytics for economic network exploration," Visual Informatics, vol. 4, no. 4, pp. 11-22, 2020.
X. Wan, X. Zuo, and F. Song, "Solving dynamic overlapping community detection problem by a multiobjective evolutionary algorithm based on decomposition," Swarm and Evolutionary Computation, vol. 54, p. 100668, 2020.
X. Zeng, W. Wang, C. Chen, and G. G. Yen, "A consensus community-based particle swarm optimization for dynamic community detection," IEEE transactions on cybernetics, vol. 50, no. 6, pp. 2502-2513, 2019.
A. Ramadan, S. Kamel, R. A. El-Sehiemy, and B. Khan, "Parameter optimization of PV modules: An overview," Active Electrical Distribution Network, pp. 401-414, 2022.
M. E. Newman and M. Girvan, "Finding and evaluating community structure in networks," Physical review E, vol. 69, no. 2, p. 026113, 2004.
W. Kulpa, A. Szymański, and M. Turzański, "Function and colorful extensions of the KKM theorem," 2020.
X. Zhou, Y. Liu, B. Li, and H. Li, "A multiobjective discrete cuckoo search algorithm for community detection in dynamic networks," Soft Computing, vol. 21, pp. 6641-6652, 2017.
R. Zhang, X. Jiang, and R. Li, "Improved decomposition-based multi-objective cuckoo search algorithm for spectrum allocation in cognitive vehicular network," Physical Communication, vol. 34, pp. 301-309, 2019.
S. Aslam, M. P. Michaelides, and H. Herodotou, "Dynamic and Continuous Berth Allocation using Cuckoo Search Optimization," in VEHITS, 2021, pp. 72-81.
F. Folino and C. Pizzuti, "An evolutionary multiobjective approach for community discovery in dynamic networks," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 8, pp. 1838-1852, 2013.
X. S. Yang, S. Deb, and S. K. Mishra, "Multi-species cuckoo search algorithm for global optimization Cogn," Comput, vol. 10, no. 6, p. 1085, 2018.
B. Yang, "Multi‐objective optimization of integrated gas–electricity energy system based on improved multi‐object cuckoo algorithm," Energy Science & Engineering, vol. 9, no. 10, pp. 1839-1857, 2021.
T. Chakraborty, S. Srinivasan, N. Ganguly, S. Bhowmick, and A. Mukherjee, "Constant communities in complex networks," Scientific reports, vol. 3, no. 1, p. 1825, 2013.
W.-Y. Chiu, G. G. Yen, and T.-K. Juan, "Minimum manhattan distance approach to multiple criteria decision making in multiobjective optimization problems," IEEE Transactions on Evolutionary Computation, vol. 20, no. 6, pp. 972-985, 2016.
D. Mandaglio, A. Amelio, and A. Tagarelli, "Consensus community detection in multilayer networks using parameter-free graph pruning," in Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part III 22, 2018: Springer, pp. 193-205.
D. Saha and P. S. Mandal, "A Distributed Algorithm for Overlapped Community Detection in Large-Scale Networks," in 2021 International Conference on COMmunication Systems & NETworkS (COMSNETS), 2021: IEEE, pp. 483-491.
I. Yakupov and M. Buzdalov, "On asynchronous non-dominated sorting for steady-state multiobjective evolutionary algorithms," in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018, pp. 205-206.
C. Farage, D. Edler, A. Eklöf, M. Rosvall, and S. Pilosof, "Identifying flow modules in ecological networks using Infomap," Methods in Ecology and Evolution, vol. 12, no. 5, pp. 778-786, 2021.
D. Greene, D. Doyle, and P. Cunningham, "Tracking the evolution of communities in dynamic social networks," in 2010 international conference on advances in social networks analysis and mining, 2010: IEEE, pp. 176-183.
