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2017 | OriginalPaper | Buchkapitel

1. Introduction

verfasst von : Maoguo Gong, Qing Cai, Lijia Ma, Shanfeng Wang, Yu Lei

Erschienen in: Computational Intelligence for Network Structure Analytics

Verlag: Springer Singapore

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Abstract

Complex network structure analytics contribute greatly to the understanding of complex systems, such as Internet, social network, and biological network. Many issues in network structure analytics, for example, community detection, structure balance, and influence maximization, can be formulated as optimization problems. These problems usually are NP-hard and nonconvex, and generally cannot be well solved by canonical optimization techniques. Computational intelligence-based algorithms have been proved to be effective and efficient for network structure analytics. This chapter gives a holistic overview of complex networks and the emerging topics concerning network structure analytics as well as some basic optimization models for network issues.

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Nächstes Kapitel Network Community Discovery with Evolutionary Single-Objective Optimization
Literatur
1.
Agrawal, R.: Bi-objective community detection (bocd) in networks using genetic algorithm. In: Contemporary Computing, pp. 5–15. Springer (2011)
2.
Amelio, A., Pizzuti, C.: Community mining in signed networks: a multiobjective approach. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 95–99. ACM (2013)
3.
Amiri, B., Hossain, L., Crawford, J.W., Wigand, R.T.: Community detection in complex networks: multi-objective enhanced firefly algorithm. Knowl. Based Syst. 46, 1–11 (2013)CrossRef
4.
Arenas, A., Duch, J., Fernández, A., Gómez, S.: Size reduction of complex networks preserving modularity. New J. Phs. 9(6), 176 (2007)
5.
Arenas, A., Fernandez, A., Fortunato, S., Gomez, S.: Motif-based communities in complex networks. J. Phs. A: Math. Theor. 41(22), 224,001 (2008)
6.
Arenas, A., Fernández, A., Gomez, S.: Analysis of the structure of complex networks at different resolution levels. New J. Phys. 10(5), 053,039 176 (2008)
7.
Boguñá, M., Pastor-Satorras, R., Díaz-Guilera, A., Arenas, A.: Models of social networks based on social distance attachment. Phys. Rev. E 70(5), 056,122 (2004)
8.
9.
Butun, E., Kaya, M.: A multi-objective genetic algorithm for community discovery. In: 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), vol. 1, pp. 287–292. IEEE (2013)
10.
Cai, Q., Gong, M., Ma, L., Jiao, L.: A novel clonal selection algorithm for community detection in complex networks. Comput Intell. 31(3), 442–464 (2015)
11.
Cai, Q., Gong, M., Shen, B., Ma, L., Jiao, L.: Discrete particle swarm optimization for identifying community structures in signed social networks. Neural Netw. 58, 4–13 (2014)CrossRef
12.
Cai, Q., Gong, M., Ma, L., Jiao, L.: A novel clonal selection algorithm for community detection in complex networks. Comput. Intell. 31(3), 442–464 (2015)MathSciNetCrossRef
13.
Cai, Q., Gong, M., Ma, L., Ruan, S., Yuan, F., Jiao, L.: Greedy discrete particle swarm optimization for large-scale social network clustering. Inf. Sci. 316, 503–516 (2015)CrossRef
14.
Cai, Q., Gong, M., Ruan, S., Miao, Q., Du, H.: Network structural balance based on evolutionary multiobjective optimization: a two-step approach. IEEE Trans. Evol. Comput. 19(6), 903–916 (2015)CrossRef
15.
Cao, C., Ni, Q., Zhai, Y.: A novel community detection method based on discrete particle swarm optimization algorithms in complex networks. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 171–178. IEEE (2015)
16.
Chen, G., Wang, Y., Wei, J.: A new multiobjective evolutionary algorithm for community detection in dynamic complex networks. Math. Probl. Eng. 2013 (2013)
17.
Chen, G., Wang, Y., Yang, Y.: Community detection in complex networks using immune clone selection algorithm. Int. J. Digital Content Technol. Appl. 5(6), 182–189 (2011)CrossRef
18.
Duch, J., Arenas, A.: Community detection in complex networks using extremal optimization. Phys. Rev. E 72(2), 027,104 (2005)
19.
Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press (2010)
20.
Facchetti, G., Iacono, G., Altafini, C.: Computing global structural balance in large-scale signed social networks. Proc. Natl. Acad. Sci. 108(52), 20953–20958 (2011)CrossRef
21.
Folino, F., Pizzuti, C.: A multiobjective and evolutionary clustering method for dynamic networks. In: 2010 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 256–263. IEEE (2010)
22.
Folino, F., Pizzuti, C.: Multiobjective evolutionary community detection for dynamic networks. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 535–536. ACM (2010)
23.
Folino, F., Pizzuti, C.: An evolutionary multiobjective approach for community discovery in dynamic networks. IEEE Trans. Knowl. Data Eng. 26(8), 1838–1852 (2014)CrossRef
24.
25.
Fortunato, S., Barthelemy, M.: Resolution limit in community detection. Proc. Natl. Acad. Sci. 104(1), 36–41 (2007)CrossRef
26.
Gach, O., Hao, J.K.: A memetic algorithm for community detection in complex networks. In: International Conference on Parallel Problem Solving from Nature, pp. 327–336. Springer (2012)
27.
Geem, Z.W., Kim, J.H., Loganathan, G.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRef
28.
Ghorbanian, A., Shaqaqi, B.: A genetic algorithm for modularity density optimization in community detection (2015)
29.
Ghosn, F., Palmer, G., Bremer, S.A.: The mid3 data set, 1993–2001: procedures, coding rules, and description. Conflict Manag. Peace Sci. 21(2), 133–154 (2004)CrossRef
30.
Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)MathSciNetCrossRefMATH
31.
Gleiser, P.M., Danon, L.: Community structure in jazz. Adv. Complex Sys. 6(04), 565–573 (2003)CrossRef
32.
Gog, A., Dumitrescu, D., Hirsbrunner, B.: Community detection in complex networks using collaborative evolutionary algorithms. In: European Conference on Artificial Life, pp. 886–894. Springer (2007)
33.
Gómez, S., Jensen, P., Arenas, A.: Analysis of community structure in networks of correlated data. Phys. Rev. E 80(1), 016,114 (2009)
34.
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)
35.
Gong, M., Cai, Q., Li, Y., Ma, J.: An improved memetic algorithm for community detection in complex networks. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2012)
36.
Gong, M., Chen, X., Ma, L., Zhang, Q., Jiao, L.: Identification of multi-resolution network structures with multi-objective immune algorithm. Appl. Soft Comp. 13(4), 1705–1717 (2013)
37.
Gong, M., Ma, L., Zhang, Q., Jiao, L.: Community detection in networks by using multiobjective evolutionary algorithm with decomposition. Phys. A: Stat. Mech. Appl. 391(15), 4050–4060 (2012)CrossRef
38.
Gong, M.G., Zhang, L.J., Ma, J.J., Jiao, L.C.: Community detection in dynamic social networks based on multiobjective immune algorithm. J. Comput. Sci. Technol. 27(3), 455–467 (2012)MathSciNetCrossRefMATH
39.
Gong, M., Fu, B., Jiao, L., Du, H.: Memetic algorithm for community detection in networks. Phys. Rev. E 84(5), 056,101 (2011)
40.
Gong, M., Hou, T., Fu, B., Jiao, L.: A non-dominated neighbor immune algorithm for community detection in networks. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 1627–1634. ACM (2011)
41.
Guimera, R., Danon, L., Diaz-Guilera, A., Giralt, F., Arenas, A.: Self-similar community structure in a network of human interactions. Phys. Rev. E 68(6), 065,103 (2003)
42.
Guimerà, R., Sales-Pardo, M., Amaral, L.A.N.: Module identification in bipartite and directed networks. Phys. Rev. E 76(3), 036,102 (2007)
43.
Guoqiang, C., Xiaofang, G.: A genetic algorithm based on modularity density for detecting community structure in complex networks. In: 2010 International Conference on Computational Intelligence and Security (CIS), pp. 151–154. IEEE (2010)
44.
He, D., Wang, Z., Yang, B., Zhou, C.: Genetic algorithm with ensemble learning for detecting community structure in complex networks. In: Fourth International Conference on Computer Sciences and Convergence Information Technology, 2009. ICCIT’09, pp. 702–707. IEEE (2009)
45.
Heider, F.: Attitudes and cognitive organization. J. Psychol. 21(1), 107–112 (1946)CrossRef
46.
Holme, P., Kim, B.J., Yoon, C.N., Han, S.K.: Attack vulnerability of complex networks. Phys. Rev. E 65(5), 056,109 (2002)
47.
Huang, Q., White, T., Jia, G., Musolesi, M., Turan, N., Tang, K., He, S., Heath, J.K., Yao, X.: Community detection using cooperative co-evolutionary differential evolution. In: International Conference on Parallel Problem Solving from Nature, pp. 235–244. Springer (2012)
48.
Jia, G., Cai, Z., Musolesi, M., Wang, Y., Tennant, D.A., Weber, R.J., Heath, J.K., He, S.: Community detection in social and biological networks using differential evolution. In: Learning and Intelligent Optimization, pp. 71–85. Springer (2012)
49.
Jin, D., He, D., Liu, D., Baquero, C.: Genetic algorithm with local search for community mining in complex networks. In: 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2010, vol. 1, pp. 105–112. IEEE (2010)
50.
Kim, K., McKay, R.I., Moon, B.R.: Multiobjective evolutionary algorithms for dynamic social network clustering. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 1179–1186. ACM (2010)
51.
Kropivnik, S., Mrvar, A.: An analysis of the slovene parliamentary parties network. In: Ferligoj, A. Kramberger, A. (eds.) Developments in Statistics and Methodology, pp. 209–216 (1996)
52.
Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. 11(3), 033,015 (2009)
53.
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046,110 (2008)
54.
Lázár, A., Ábel, D., Vicsek, T.: Modularity measure of networks with overlapping communities. EPL(Europhysics Letters) 90(1), 18,001 (2010)
55.
Leicht, E.A., Newman, M.E.: Community structure in directed networks. Phys. Rev. Lett. 100(11), 118,703 (2008)
56.
Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1361–1370 (2010)
57.
Leskovec, J., Kleinberg, J., Faloutsos, C.: Graph evolution: Densification and shrinking diameters. ACM Trans. Knowl. Discov. Data 1(1), 2 (2007)
58.
59.
Li, J., Song, Y.: Community detection in complex networks using extended compact genetic algorithm. Soft Comput. 17(6), 925–937 (2013)
60.
Li, S., Chen, Y., Du, H., Feldman, M.W.: A genetic algorithm with local search strategy for improved detection of community structure. Complexity 15(4), 53–60 (2010)
61.
Li, X., Gao, C.: A novel community detection algorithm based on clonal selection. J. Comput. Inf. Sys. 9(5), 1899–1906 (2013)
62.
Li, Y., Liu, G., Lao, S.y.: Complex network community detection algorithm based on genetic algorithm. In: The 19th International Conference on Industrial Engineering and Engineering Management, pp. 257–267. Springer (2013)
63.
Li, Z., Zhang, S., Wang, R.S., Zhang, X.S., Chen, L.: Quantitative function for community detection. Phys. Rev. E 77(3), 036,109 (Mar. 2008)
64.
Li, Y., Liu, J., Liu, C.: A comparative analysis of evolutionary and memetic algorithms for community detection from signed social networks. Soft Comput. 18(2), 329–348 (2014)CrossRef
65.
Lin, C.C., Liu, W.Y., Deng, D.J.: A genetic algorithm approach for detecting hierarchical and overlapping community structure in dynamic social networks. In: 2013 IEEE Wireless Communications and Networking Conference (WCNC), pp. 4469–4474. IEEE (2013)
66.
Lipczak, M., Milios, E.: Agglomerative genetic algorithm for clustering in social networks. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 1243–1250. ACM (2009)
67.
Liu, C., Liu, J., Jiang, Z.: A multiobjective evolutionary algorithm based on similarity for community detection from signed social networks. IEEE Trans. Cybernet. 44(12), 2274–2287 (2014)
68.
Liu, J., Zhong, W., Abbass, H.A., Green, D.G.: Separated and overlapping community detection in complex networks using multiobjective evolutionary algorithms. In: 2010 IEEE Congress on Evolutionary Computation (CEC 2010), pp. 1–7. IEEE (2010)
69.
Liu, X., Li, D., Wang, S., Tao, Z.: Effective algorithm for detecting community structure in complex networks based on ga and clustering. In: International Conference on Computational Science, pp. 657–664. Springer (2007)
70.
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)CrossRef
71.
Ma, L., Gong, M., Cai, Q., Jiao, L.: Enhancing community integrity of networks against multilevel targeted attacks. Phys. Rev. E 88(2), 022,810 (2013)
72.
Ma, L., Gong, M., Liu, J., Cai, Q., Jiao, L.: Multi-level learning based memetic algorithm for community detection. Appl. Soft Comput. 19, 121–133 (2014)CrossRef
73.
Ma, L., Gong, M., Du, H., Shen, B., Jiao, L.: A memetic algorithm for computing and transforming structural balance in signed networks. Knowl. Based Sys. 85, 196–209 (2015)CrossRef
74.
Massen, C.P., Doye, J.P.: Identifying communities within energy landscapes. Phys. Rev. E 71(4), 046,101 (2005)
75.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827 (2002)CrossRef
76.
Mu, C.H., Xie, J., Liu, Y., Chen, F., Liu, Y., Jiao, L.C.: Memetic algorithm with simulated annealing strategy and tightness greedy optimization for community detection in networks. Appl. Soft Comput. 34, 485–501 (2015)CrossRef
77.
Naeni, L.M., Berretta, R., Moscato, P.: Ma-net: A reliable memetic algorithm for community detection by modularity optimization. In: Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, vol. 1, pp. 311–323. Springer (2015)
78.
Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)
79.
Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)CrossRef
80.
Nicosia, V., Mangioni, G., Carchiolo, V., Malgeri, M.: Extending the definition of modularity to directed graphs with overlapping communities. J. Stat. Mech.: Theory Exp. 2009(03), P03,024 (2009)
81.
Oda, K., Kimura, T., Matsuoka, Y., Funahashi, A., Muramatsu, M., Kitano, H.: Molecular interaction map of a macrophage. AfCS Res. Rep. 2(14), 1–12 (2004)
82.
Oda, K., Matsuoka, Y., Funahashi, A., Kitano, H.: A comprehensive pathway map of epidermal growth factor receptor signaling. Mol. Syst. Biol. 1(1), 2005 (2005)CrossRef
83.
Pizzuti, C.: Ga-net: a genetic algorithm for community detection in social networks. In: Parallel Problem Solving from Nature (PPSN), vol. 5199, pp. 1081–1090. Springer (2008)
84.
Pizzuti, C.: A multi-objective genetic algorithm for community detection in networks. In: 21st International Conference on Tools with Artificial Intelligence, 2009. ICTAI’09, pp. 379–386. IEEE (2009)
85.
Pizzuti, C.: Overlapped community detection in complex networks. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 859–866. ACM (2009)
86.
Pizzuti, C.: A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans. Evol. Comput. 16(3), 418–430 (2012)CrossRef
87.
Pons, P., Latapy, M.: Post-processing hierarchical community structures: quality improvements and multi-scale view. Theor. Comput. Sci. 412(8), 892–900 (2011)
88.
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. Proc. Natil. Acad. Sci. USA 101(9), 2658–2663 (2004)CrossRef
89.
Read, K.E.: Cultures of the central highlands, New Guinea. Southwestern J. Anthropol. pp. 1–43 (1954)
90.
Rees, B.S., Gallagher, K.B.: Overlapping community detection using a community optimized graph swarm. Soc. Netw. Anal. Min. 2(4), 405–417 (2012)CrossRef
91.
Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Phys. Rev. E 74(1), 016,110 (2006)
92.
Rosvall, M., Bergstrom, C.T.: An information-theoretic framework for resolving community structure in complex networks. Proc. Natl. Acad. Sci. 104(18), 7327–7331 (2007)CrossRef
93.
Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. 105(4), 1118–1123 (2008)CrossRef
94.
Salgado, H., Gama-Castro, S., Peralta-Gil, M., Díaz-Peredo, E., Sánchez-Solano, F., Santos-Zavaleta, A., Martínez-Flores, I., Jiménez-Jacinto, V., Bonavides-Martínez, C., Segura-Salazar, J., et al.: Regulondb (version 5.0): Escherichia coli k-12 transcriptional regulatory network, operon organization, and growth conditions. Nucleic Acids Res. 34(suppl 1), D394–D397 (2006)CrossRef
95.
Schneider, C.M., Moreira, A.A., Andrade, J.S., Havlin, S., Herrmann, H.J.: Mitigation of malicious attacks on networks. Proc. Natl. Acad. Sci. 108(10), 3838–3841 (2011)CrossRef
96.
Shang, R., Bai, J., Jiao, L., Jin, C.: Community detection based on modularity and an improved genetic algorithm. Phys. A: Stat. Mech. Appl. 392(5), 1215–1231 (2013)CrossRef
97.
Shang, R., Luo, S., Zhang, W., Stolkin, R., Jiao, L.: A multiobjective evolutionary algorithm to find community structures based on affinity propagation. Phys. A: Stat. Mech. Appl. 453, 203–227 (2016)CrossRef
98.
Shelokar, P., Quirin, A., Cordón, Ó.: Three-objective subgraph mining using multiobjective evolutionary programming. J. Comput. Syst. Sci. 80(1), 16–26 (2014)MathSciNetCrossRefMATH
99.
Shen, H., Cheng, X., Cai, K., Hu, M.B.: Detect overlapping and hierarchical community structure in networks. Phys. A: Stat. Mech. Appl. 388(8), 1706–1712 (2009)CrossRef
100.
Shi, C., Yan, Z., Cai, Y., Wu, B.: Multi-objective community detection in complex networks. Appl. Soft Comput. 12(2), 850–859 (2012)
101.
Shi, C., Yu, P.S., Cai, Y., Yan, Z., Wu, B.: On selection of objective functions in multi-objective community detection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 2301–2304. ACM (2011)
102.
Shi, Z., Liu, Y., Liang, J.: Pso-based community detection in complex networks. In: Second International Symposium on Knowledge Acquisition and Modeling, 2009. KAM’09, vol. 3, pp. 114–119. IEEE (2009)
103.
Shi, C., Yu, P.S., Yan, Z., Huang, Y., Wang, B.: Comparison and selection of objective functions in multiobjective community detection. Comput. Intell. 30(3), 562–582 (2014)MathSciNetCrossRef
104.
Sun, Y., Du, H., Gong, M., Ma, L., Wang, S.: Fast computing global structural balance in signed networks based on memetic algorithm. Phys. A: Stat. Mech. Appl. 415, 261–272 (2014)MathSciNetCrossRef
105.
Tasgin, M., Herdagdelen, A., Bingol, H.: Community detection in complex networks using genetic algorithms. arXiv:​0711.​0491 (2007)
106.
Wang, S., Gong, M., Du, H., Ma, L., Miao, Q., Du, W.: Optimizing dynamical changes of structural balance in signed network based on memetic algorithm. Social Netw. 44, 64–73 (2016)CrossRef
107.
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications, vol. 8. Cambridge University Press (1994)
108.
Watts, D.J., Strogatz, S.H.: Collective dynamics of small-worldnetworks. Nature 393(6684), 440–442 (1998)CrossRefMATH
109.
Wu, P., Pan, L.: Multi-objective community detection based on memetic algorithm. PLoS one 10(5), e0126,845 (2015)
110.
Xiaodong, D., Cunrui, W., Xiangdong, L., Yanping, L.: Web community detection model using particle swarm optimization. In: IEEE Congress on Evolutionary Computation, 2008. CEC 2008, pp. 1074–1079. IEEE (2008)
111.
Yang, B., Cheung, W.K., Liu, J.: Community mining from signed social networks. IEEE Trans. Knowl. Data Eng. 19(10), 1333–1348 (2007)CrossRef
112.
Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)CrossRef
113.
Zeng, A., Liu, W.: Enhancing network robustness against malicious attacks. Phys. Rev. E 85(6), 066,130 (2012)
114.
Zhan, W., Guan, J., Chen, H., Niu, J., Jin, G.: Identifying overlapping communities in networks using evolutionary method. Phys. A: Stat. Mech. Appl. 442, 182–192 (2016)CrossRef
115.
Zhang, S., Wang, R.S., Zhang, X.S.: Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Phys. A: Stat. Mech. Appl. 374(1), 483–490 (2007)CrossRef
116.
Zheng, X., Zeng, D., Wang, F.Y.: Social balance in signed networks. Inf. Syst. Frontiers 17(5), 1077–1095 (2015)CrossRef
117.
Zhou, D., Wang, X.: A neighborhood-impact based community detection algorithm via discrete pso. Math. Probl. Eng. 2016 (2016)
118.
Zhou, X., Liu, Y., Zhang, J., Liu, T., Zhang, D.: An ant colony based algorithm for overlapping community detection in complex networks. Phys. A: Stat. Mech. Appl. 427, 289–301 (2015)
119.
Zhou, Y., Wang, J., Luo, N., Zhang, Z.: Multiobjective local search for community detection in networks. Soft Comput. pp. 1–10 (May 2015)
Metadaten
Titel
Introduction
verfasst von
Maoguo Gong
Qing Cai
Lijia Ma
Shanfeng Wang
Yu Lei
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Computational Intelligence for Network Structure Analytics

Print ISBN: 978-981-10-4557-8

Electronic ISBN: 978-981-10-4558-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-4558-5_1

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