research-article

Community detection in complex networks

Authors:

Muyi LinAuthors Info & Claims

Applied Soft Computing, Volume 53, Issue C

Pages 285 - 295

https://doi.org/10.1016/j.asoc.2017.01.005

Published: 01 April 2017 Publication History

Abstract

Display Omitted A novel multi-objective discrete backtracking search optimization algorithm with decomposition is proposed for community detection in complex networks.We present a discrete variant of backtracking search optimization algorithm (BSA) where the updating rules of individuals are redesigned based on the network topology.A novel multi-objective discrete method (MODBSA/D) based on the proposed discrete variant DBSA is first proposed to minimize two objective functions in terms of Negative Ratio Association (NRA) and Ratio Cut (RC) of community detection problem.The proposed algorithm is tested on some real-world networks to evaluate its performance. Community detection is believed to be a very important tool for understanding both the structure and function of complex networks, and has been intensively investigated in recent years. Community detection can be considered as a multi-objective optimization problem and the nature-inspired optimization techniques have shown promising results in dealing with this problem. In this study, we present a novel multi-objective discrete backtracking search optimization algorithm with decomposition for community detection in complex networks. First, we present a discrete variant of the backtracking search optimization algorithm (DBSA) where the updating rules of individuals are redesigned based on the network topology. Then, a novel multi-objective discrete method (MODBSA/D) based on the proposed discrete variant DBSA is first proposed to minimize two objective functions in terms of Negative Ratio Association (NRA) and Ratio Cut (RC) of community detection problems. Finally, the proposed algorithm is tested on some real-world networks to evaluate its performance. The results clearly show that MODBSA/D has effective and promising performance for dealing with community detection in complex networks.

References

[1]

D.J. Watts, A twenty-first century science, Nature, 445 (2007) 489.

[2]

S. Suweis, F. Simini, J.R. Banavar, A. Maritan, Emergence of structural and dynamical properties of ecological mutualistic networks, Nature, 500 (2013) 449-452.

[3]

A. Clauset, M.E.J. Newman, C. Moore, Finding community structure in very large networks, Phys. Rev. E, 70 (2004) 066111.

[4]

M. Rosvall, C.T. Bergstrom, Maps of random walks on complex networks reveal community structure, Proc. Natl. Acad. Sci., 105 (2008) 1118-1123.

[5]

A.L. Barabsi, Scale-free networks: adecade and beyond, Science, 325 (2009) 412-413.

[6]

S. Fortunato, Community detection in graphs, Phys. Rep., 486 (2010) 75-174.

[7]

M.E.J. Newman, Fast algorithm for detecting community structure in networks, Phys. Rev. E, 69 (2004) 066133.

[8]

M.E.J. Newman, Modularity and community structure in networks, Proc. Natl. Acad. Sci. U. S. A., 103 (2006) 8577-8582.

[9]

D. Martin, R. del Toro, R. Haber, Optimal tuning of a networked linear controller using a multi-objective genetic algorithm and its application to one complex electromechanical process, Int. J. Innov. Comput. Inf. Control, 5 (2009) 3405-3414.

[10]

R.C. David, R.E. Precup, E.M. Petriu, Gravitational search algorithm-based design of fuzzy control systems with a reduced parametric sensitivity, Inf. Sci., 247 (2013) 154-173.

Digital Library

[11]

R. Allmendinger, J. Handl, J. Knowles, Multiobjective optimization: when objectives exhibit non-uniform latencies, Eur. J. Oper. Res., 243 (2015) 497-513.

[12]

I.P. Solos, I.X. Tassopoulos, G.N. Beligiannis, Optimizing shift scheduling for tank trucks using an effective stochastic variable neighbourhood approach, Int. J. Artif. Intell., 14 (2016) 1-26.

[13]

Y. Zhang, L. Wu, Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network, Expert Syst. Appl., 36 (2009) 8849-8854.

Digital Library

[14]

Y. Zhang, J. Yan, G. Wei, Find multi-objective paths in stochastic networks via chaotic immune PSO, Expert Syst. Appl., 37 (2010) 1911-1919.

Digital Library

[15]

Y. Zhang, L. Wu, G. Wei, A novel algorithm for all pairs shortest path problem based on matrix multiplication and pulse coupled neural network, Digital Signal Process., 21 (2011) 517-521.

Digital Library

[16]

Y. Zhang, S. Wang, G. Ji, A comprehensive survey on particle swarm optimization algorithm and its applications, Math. Problems Eng., 2015 (2015) 1-38.

[17]

M.S. Rahman, A. Ngom, A Fast Agglomerative Community Detection Method for Protein Complex Discovery in Protein Interaction Networks. Pattern Recognition in Bioinformatics, Springer, Berlin, Heidelberg, 2013.

Digital Library

[18]

G.W. Flake, S. Lawrence, C.L. Giles, Self-organization and identification of web communities, Computer, 35 (2002) 66-70.

Digital Library

[19]

C. Pizzuti, A multiobjective genetic algorithm to find communities in complex networks, IEEE Trans. Evol. Comput., 6 (2012) 418-430.

Digital Library

[20]

C. Shi, Z. Yan, Y. Cai, Multi-objective community detection in complex networks, Appl. Soft Comput., 12 (2012) 850-859.

Digital Library

[21]

G.A. Ezhilarasi, K.S. Swarup, Network decomposition using KernighanLin strategy aided harmony search algorithm, Swarm Evol. Comput., 7 (2012) 1-6.

[22]

B. Amiri, L. Hossain, J.W. Crawford, Community detection in complex networks: multi-objective enhanced firefly algorithm, Knowl. Syst., 46 (2013) 1-11.

Digital Library

[23]

M. Gong, Q. Cai, X. Chen, L. Ma, Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition, IEEE Trans. Evol. Comput., 18 (2014) 82-97.

[24]

L. Ma, M. Gong, J. Liu, Multi-level learning based memetic algorithm for community detection, Appl. Soft Comput., 19 (2014) 121-133.

[25]

X. Zhou, Y. Liu, B. Li, Multiobjective biogeography based optimization algorithm with decomposition for community detection in dynamic networks, Physica A, 436 (2015) 430-442.

[26]

E.A. Hassan, A.I. Hafez, A.E. Hassanien, Community Detection Algorithm Based on Artificial Fish Swarm Optimization. Intelligent Systems 2014, Springer International Publishing, 2015.

[27]

C.H. Mu, J. Xie, Y. Liu, Memetic algorithm with simulated annealing strategy and tightness greedy optimization for community detection in networks, Appl. Soft Comput., 34 (2015) 485-501.

Digital Library

[28]

C. Shi, Z.Y. Yan, Y. Wang, A genetic algorithm for detecting communities in large-scale complex networks, Adv. Complex Syst., 13 (2010) 3-17.

[29]

P. Civicioglu, Backtracking search optimization algorithm for numerical optimization problems, Appl. Math. Comput., 219 (2013) 8121-8144.

Digital Library

[30]

R.E. Precup, A.D. Balint, M.B. Radac, Backtracking Search Optimization Algorithm-based approach to PID controller tuning for torque motor systems, Syst. Conf. IEEE (2015) 127-132.

[31]

R.E. Precup, A.D. Balint, E.M. Petriu, PI and PID controller tuning for an automotive application using backtracking search optimization algorithms, IEEE, Jubilee International Symposium on Applied Computational Intelligence and Informatics (2015) 185-197.

[32]

D. Chen, F. Zou, R. Lu, Learning backtracking search optimisation algorithm and its application, Inf. Sci., 376 (2017) 71-94.

Digital Library

[33]

Mostafa Modiri-Delshad, S. Hr, Aghay Kaboli, Ehsan Taslimi-Renani, Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options, Energy, 116 (2016) 637-649.

[34]

H. Duan, Q. Luo, Adaptive backtracking search algorithm for induction magnetometer optimization, IEEE Trans. Magn., 50 (2014) 1-6.

[35]

A. EI-Fergany, Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm, Int. J. Electr. Power Energy Syst., 64 (2015) 1197-1205.

[36]

S.K. Agarwal, S. Shah, R. Kumar, Classification of mental tasks from EEG data using backtracking search optimization based neural classifier, Neurocomputing, 166 (2015) 397-403.

Digital Library

[37]

J. Lin, Oppositional backtracking search optimization algorithm for parameter identification of hyperchaotic systems, Nonlinear Dyn., 80 (2015) 209-219.

[38]

A.E. Chaib, H.R.E.H. Bouchekara, R. Mehasni, Optimal power flow with emission and non-smooth cost functions using backtracking search optimization algorithm, Int. J. Electr. Power Energy Syst., 81 (2016) 64-77.

[39]

Z. Su, H. Wang, P. Yao, A hybrid backtracking search optimization algorithm for nonlinear optimal control problems with complex dynamic constraints, Neurocomputing, 186 (2016) 182-194.

Digital Library

[40]

F. Radicchi, C. Castellano, F. Cecconi, Defining and identifying communities in networks, Proc. Natl. Acad. Sci. U. S. A., 101 (2004) 2658-2663.

[41]

Z. Li, S. Zhang, R. Wang, Quantitative function for community detection, Phys. Rev. E, 77 (2008) 036109.

[42]

M. Gong, L. Ma, Q. Zhang, L. Jiao, Community detection in networks by using multi-objective evolutionary algorithm with decomposition, Physica A, 391 (2012) 4050-4060.

[43]

Y. Zhou, J. Wang, N. Luo, Multiobjective local search for community detection in networks, Soft Comput., 20 (2016) 3273-3282.

Digital Library

[44]

C. Liu, J. Liu, Z. Jiang, A multiobjective evolutionary algorithm based on similarity for community detection from signed social networks, IEEE Trans. Cybern., 44 (2014) 2274-2287.

[45]

C. Shi, P.S. Yu, Z. Yan, Comparison and selection of objective functions in multiobjective community detection, Comput. Intell., 30 (2014) 562-582.

Digital Library

[46]

X. Zhou, Y. Liu, B. Li, A multiobjective discrete cuckoo search algorithm for community detection in dynamic networks, Soft Comput. (2016) 1-12.

[47]

Z. Li, L. He, Y. Li, A novel multiobjective particle swarm optimization algorithm for signed network community detection, Appl. Intell., 44 (2016) 621-633.

Digital Library

[48]

W.A. Hariz, M.F. Abdulhalim, Improving the performance of evolutionary multi-objective co-clustering models for community detection in complex social networks, Swarm Evol. Comput., 26 (2016) 137-156.

[49]

J. Kennedy, R.C. Eberhart, A discrete binary version of the particle swarm algorithm, IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation (1997) 4104-4108.

[50]

Q. Zhang, H. Li, MOEA/D: a multiobjective evolutionary algorithm based on decomposition, IEEE Trans. Evol. Comput., 11 (2007) 712-731.

Digital Library

[51]

M.E.J. Newman, Finding community structure in networks using the eigenvectors of matrices, Phys. Rev. E, 74 (2006) 036104.

[52]

L. Danon, A. Diaz-Guilera, J. Duch, Comparing community structure identification, J. Stat. Mech: Theory Exp., 2005 (2005).

[53]

W.W. Zachary, An information flow model for conflict and fission in small groups, J. Anthropol. Res. (1977) 452-473.

[54]

M. Girvan, M.E.J. Newman, Community structure in social and biological networks, Proc. Natl. Acad. Sci., 99 (2002) 7821-7826.

[55]

M. Newman, Netdata, 2009. http://www-personal.umich.edu/mejn/netdata/

[56]

V. Krebs, http://www.orgnet.com/.

[57]

J. Su, T.C. Havens, Quadratic program-based modularity maximization for fuzzy community detection in social networks, IEEE Trans. Fuzzy Syst., 23 (2015) 1356-1371.

[58]

D.J. Watts, S.H. Strogatz, Collective dynamics of small-world networks, Nature, 393 (1998) 440-442.

[59]

V. Batagelj, A. Mrvar, Pajek Datasets, 2006. http://vlado.fmf.uni-lj.si/pub/networks/data/

[60]

V.D. Blondel, J.L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks, J. Stat. Mech: Theory Exp., 10 (2008).

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cover image Applied Soft Computing

Applied Soft Computing Volume 53, Issue C

April 2017

476 pages

ISSN:1568-4946

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Copyright © Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

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Published: 01 April 2017

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Cited By

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https://dl.acm.org/doi/10.1109/MCI.2023.3277773
Ahmad YUllah MKhan RShafi BKhan AZareei MAldosary AMohamed E(2020)SiFSOComplexity10.1155/2020/66950322020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/6695032
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