- Fahong Yu ORCID:orcid.org/0000-0002-9342-64191,
- Meijia Chen1,
- Kun Deng1,
- Xiaoyun Xia1,
- Bolin Yu2,
- Huiming Gao1,
- Feng He1,
- Longhua Ma3 &
- …
- Zhao-Quan Cai4
310Accesses
Abstract
It is very important to discovery potential customers or groups for the traditional textile enterprises to enhance its’ market competitiveness. Aiming to discover the implicit characters of the textile-related trade system, A biased estimation of distribution algorithm was proposed to detect the community structure in this paper. This algorithm combined a biased search and a simulated annealing selections strategy which were used to improve both the convergence speed and the accuracy of the EDAs to discovery the communities structure for complex system by maximizing the modularity density. The biased search is efficient by taking into account an asymmetric similarity between any pairs of nodes in network according to the different characteristics and local environment of nodes. The proposed algorithm was applied to detect the community structure for a textile-related trade network with the scale-free character extracted from a set of textile companies by uniquely leveraging each node with economic behavior, and the result show that the algorithm is efficient and competent
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Boisseau OJ, Haase P, Slooten E, Lusseau D, Schneider K, Dawson SM (2003) The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav Ecol Sociobiol 54(4):396–405
Chen X, Gong M, Cai Q, Ma L (2013) Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition. IEEE Transactions on Evolutionary Computation 80(9):82–97. ISSN 1550-2376. doi:10.1103/PhysRevE.80.016114
Clara P (2013) Ga-net: a genetic algorithm for community detection in social networks. In Conference: Parallel Problem Solving from Nature-PPSN X, 2013 10th International Conference on, pp 13–17. doi:10.1007/978-3-540-87700-4_107
Duch S. Lozano and A. Arenas. Analysis of large social datasets by community detection. The European Physical Journal Special Topics, 143 (1): 257–259, 2015. ISSN 1951-6355. doi:10.1140/epjst/e2007-00098-6
Faloutsos P, Faloutsos JM, Faloutsos C (2016) On power-law relationships of the internet topology. In Applications, Technologies, Architectures, and Protocols for Computer Communication, 2016. Conference Record of the Thirty-Fourth Computer Communication, vol 1, pp 251–262 vol.1, 10. doi:10.1145/316188.316229
Fortunato S, Lancichinetti A (2012) Consensus clustering in complex networks. Sci Rep 69(2):3 –15, 9 2012. ISSN 1-7. doi:10.1038/srep00336
Fortunato S, Lancichinetti A, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 69(6):133–143, 3. ISSN 1550-2376. doi:10.1103/PhysRevE.78.046110
Fortunato S, Barthelemy M (2007) Resolution limit in community detection. In Proceedings of the National Academy of Sciences of the United States of America, pp 36–41. IEEE
Gabrielli A, Zlatić V, Caldarelli G (2010) Topologically biased random walk and community finding in networks. Physics Review E 69(2):282–294. ISSN 1550-2376. doi:10.1103/PhysRevE.69.026113
Girvan M, Newman ME (2002) Community structure in social and biological networks. In Proceedings of the National Academy of Sciences of the United States of America, pp 7821–7826. IEEE
Girvan M, Newman MEJ (2002) Community structure in social and biological networks. In Proceedings of the National Academy of Sciences of the United States of America, 2002 International Conference on, vol 3, pp 7821–7826. doi:10.1073/pnas.122653799
Girvan M, Newman ME (2004) Finding and evaluating community structure in networks. Physics Review E, 69(2):3–15. ISSN 1550-2376. doi:10.1103/PhysRevE.69.026113
Haifeng D, Shuzhuo L, Yinghui C, Marcus W, Feldman (2016) A genetic algorithm with local search strategy for improved detection of community structure. Complexity 15:53–60. ISSN 10.1002/cplx.20300
Hansen P, Liberti L, Perron S, Aloise D, Caporossi G, Ruiz M (2013) Modularity maximization in networks by variable neighborhood search. In Conference: Implementation Challenge Graph Partitioning and Graph Clustering, Proceedings of the 10th DIMACS International Conference on, pp 113–127, 2. doi:10.1090/conm/588/11705
Liu Y, Mu C, Xie J et al (2015) Memetic algorithm with simulated annealing strategy and tightness greedy optimization for community detection in networks. Appl Soft Comput 34(6):485–501
Newman MEJ, Clauset A, Moore C (2014) Finding community structure in very large networks. Phys Rev E 70(6):213–223. doi:10.1103/PhysRevE.70.066111 ISSN 2470-0045
Newman MEJ (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69(6):133–143. ISSN 1550-2376. doi:10.1103/PhysRevE.69.066133
Roberto S, Ruben A, Inaki I, Yvan S (2008) A review of estimation of distribution algorithms in bioinformatics. BioData Mining, 2 (1): 4–16, 3 2008. ISSN 1756-0381. doi:10.1186/1756-0381-1-6
Santo F (2010) Community detection in graphs. Phys Rep 486(3):75 –174. ISSN 0370-1573. doi:10.1016/j.physrep.2009.11.002
Shummet B (1994) Population based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Technical Report
Sicheng C, Tianli Y (2015) Difficulty of linkage learning in estimation of distribution algorithms. In Conference: Genetic and evolutionary computation, Proceedings of the 11th Annual conference on, pp 36–41, 9. doi:10.1145/1569901.1569957
Stephanie L, Woerner, Barbara H (2015) Big data: extending the business strategy toolbox. Journal of Information Technology 486(5):160–172. ISSN 0268-3962. doi:10.1057/jit.2014.31
Victor S, Sheng Bin GU (2016) Structural minimax probability machine. IEEE Transactions on Neural Networks and Learning Systems 295(1):1 – 11, 1 2016. ISSN 2162-237X. doi:10.1109/TNNLS.2016.2544779
Wang RS, Zhang XS, Chen L, Li Z, Zhang S (2008) Quantitative function for community detection. Phys Rev E, 69(6):133–143. ISSN 1550-2376. doi:10.1103/PhysRevE.69.066133
Wang Y, Xie S (2014) Construction of tree network with limited delivery latency in homogeneous wireless sensor networks. Wireless Personal Communications 78(1):231–246. ISSN 0929-6212. doi:10.1007/s11277-014-1748-5
Yanqing H, Zengru D, Ying F, Xiaojia L, Menghui L (2016) Detecting community structure from coherent oscillation of excitable systems. Physics A, 389(2):164–170. ISSN 0305-4470. doi:10.1103/89.75.Hc; 89.65
Yu J, Jiang Y, Jia C (2014) An efficient community detection method based on rank centrality. Physica A 392(9):2182–2194
Yu X, Wei F, Xuezhi W, Ling S (2017) A rapid learning algorithm for vehicle classification. Inf Sci 2(1):395–406. ISSN 1045-9219. doi:10.1016/j.ins.2014.10.040
Yun Z, Zhaoqing P, Sam K (2016) Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Transactions on Broadcasting 6(9):166–176. ISSN 0018-9316. doi:10.1109/TBC.2015.2419824
Zachary W (1977) An information flow modelfor conflict and fission in small groups. J Anthropol Res 33(4):452–473
Acknowledgements
This research is supported by the Zhejiang Provincial Education Department Research Foundation of China under Grant No.Y201533771 and Y201636127, Zhejiang Provincial Natural Science Foundation of China under Grant No. LY16F020027 and No.LY15F020040, the Brand Major of higher vocational education in Guangdong Province under Grant No. 2016gzpp126, and Humanity and Social Science Youth foundation of Ministry of Education of China under Grant No. 15YJCZH088. National Natural Science Foundation of China (No. 61370185), Natural Science Foundation of Guangdong Province (S2013010013432, S2013010015940), Science and Technology Planning Project of Huizhou (2014B050013016, 2014B020004023).
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College of Mathematics and Information Engineering, Jiaxing University, Zhejiang, 434023, China
Fahong Yu, Meijia Chen, Kun Deng, Xiaoyun Xia, Huiming Gao & Feng He
School of Electronics and Communication, Shenzhen institute of information technology, Shenzhen, China
Bolin Yu
Ningbo Institute of Technology, Zhejiang University, Ningbo, China
Longhua Ma
Huizhou University, Huizhou Guangdong, China
Zhao-Quan Cai
- Fahong Yu
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- Meijia Chen
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- Xiaoyun Xia
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- Longhua Ma
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Correspondence toFahong Yu.
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Yu, F., Chen, M., Deng, K.et al. Community detection in the textile-related trade network using a biased estimation of distribution algorithm.J Ambient Intell Human Comput15, 1307–1316 (2024). https://doi.org/10.1007/s12652-017-0489-1
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