Optimization-based topology identification of complex networks

doi:10.1088/1674-1056/20/11/110502

Abstract In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization-based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method.

Keywords: complex networks topology identification optimization particle swarm

Received: 28 August 2010
Revised: 23 June 2011
Accepted manuscript online:

PACS:	05.45.-a	(Nonlinear dynamics and chaos)
	05.45.Xt	(Synchronization; coupled oscillators)

Fund: Project supported by the National Natural Science Foundation for Distinguished Young Scholars of China (Grant No. 50925727) and the National Natural Science Foundation of China (Grant No. 60876022).

Cite this article:

Tang Sheng-Xue(唐圣学), Chen Li(陈丽), and He Yi-Gang(何怡刚) Optimization-based topology identification of complex networks 2011 Chin. Phys. B 20 110502

[1]	Buldyrev S V, Parshani R, Paul G, Stanley H E and Havlin S 2010 Nature 464 1025
[2]	Albert R, Jeong H and Barabasi A L 1999 Nature 410 130
[3]	Jeong H, Tombor B, Albert R, Oltvai Z N and Barabasi A L 2000 Nature 407 651
[4]	Zhu Z T, Zhou J, Li P and Chen X G 2008 Chin. Phys. B 17 2874
[5]	Zhang Z, Fu Z Q and Yan G 2009 Chin. Phys. B 18 2209
[6]	Chen G, Zhou J and Liu Z 2004 Int. J. Bifur. Chaos 14 2229
[7]	Barabasi A L and Albert R 1999 Science 286 509
[8]	Watts D J and Strogatz S H 1998 Nature 393 440
[9]	Boccaletti S, Latora V, Moreno Y, Chavezf M and Hwang D U 2006 Phys. Rep. 424 175
[10]	Butts C T 2009 Science 325 414
[11]	Wu J S, Jiao L C and Chen G R 2011 Chin. Phys. B 20 060503
[12]	Pei W D, Chen Z Q and Yuan Z Z 2008 Chin. Phys. B 17 373
[13]	Zhou J and Lu J A 2007 Physica A 386 481
[14]	Liu H, Song Y R, Fan C X and Jiang G P 2010 Chin. Phys. B 19 070508
[15]	Xu Y H, Zhou W N, Fang J A and Lu H Q 2009 Phys. Lett. A 374 272
[16]	Chen L, Lu J A and Tse C K 2009 IEEE. Trans. Circuit Syst. II 56 310
[17]	Wu X Q 2008 Physica A 387 997
[18]	Hong W Y, Suo W and Ping G 2010 Acta Electron. Sin. 38 1065 (in Chinese)
[19]	Liu H, Lu J A, Lu J H and Hill D J 2009 Automatica 45 1799
[20]	Kennedy J and Eberhart R 1995 Proceedings of IEEE International Conference on Neural Networks (Piscataway: IEEE) p. 1942
[21]	Xi M L, Sun J and Xu W B 2008 Appl. Math. Comput. 205 751
[22]	Fang W, Sun J, Xie Z P and Xu W B 2010 Acta Phys. Sin. 59 3686 (in Chinese)
[23]	Sun J, Xu W B and Feng B 2005 IEEE International Conference on Systems, Man and Cybernetics (Hawaii: IEEE) p. 3049

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