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Chin. Phys. B, 2017, Vol. 26(2): 020201    DOI: 10.1088/1674-1056/26/2/020201
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Ranking important nodes in complex networks by simulated annealing

Yu Sun(孙昱)1, Pei-Yang Yao(姚佩阳)1, Lu-Jun Wan(万路军)2, Jian Shen(申健)1, Yun Zhong(钟赟)1
1 Information and Navigation College, Air Force Engineering University, Xi'an 710077, China;
2 Air Traffic Control and Navigation College, Air Force Engineering University, Xi'an 710077, China
Abstract  In this paper, based on simulated annealing a new method to rank important nodes in complex networks is presented. First, the concept of an importance sequence (IS) to describe the relative importance of nodes in complex networks is defined. Then, a measure used to evaluate the reasonability of an IS is designed. By comparing an IS and the measure of its reasonability to a state of complex networks and the energy of the state, respectively, the method finds the ground state of complex networks by simulated annealing. In other words, the method can construct a most reasonable IS. The results of experiments on real and artificial networks show that this ranking method not only is effective but also can be applied to different kinds of complex networks.
Keywords:  complex networks      node importance      ranking method      simulated annealing  
Received:  15 July 2016      Revised:  31 October 2016      Accepted manuscript online: 
PACS:  02.10.Ox (Combinatorics; graph theory)  
  89.20.Ff (Computer science and technology)  
  89.75.Fb (Structures and organization in complex systems)  
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61573017) and the Natural Science Foundation of Shaanxi Province, China (Grant No. 2016JQ6062).
Corresponding Authors:  Pei-Yang Yao     E-mail:  suny.z@qq.com

Cite this article: 

Yu Sun(孙昱), Pei-Yang Yao(姚佩阳), Lu-Jun Wan(万路军), Jian Shen(申健), Yun Zhong(钟赟) Ranking important nodes in complex networks by simulated annealing 2017 Chin. Phys. B 26 020201

[1] Sheikhahmadia A, Nematbakhsha M A and Shokrollahi A 2015 Physica A 436 833
[2] Zhao J, Yu L, Li J R and Zhou P 2015 Chin. Phys. B 24 058904
[3] Yu Y and Fan S H 2015 J. Inform. Comput. Sci. 12 1281
[4] Singh A, Kumar R and Singh Y N 2015 Acta Phys. Polon. B 46 305
[5] Nie T Y, Guo Z, Zhao K and Lu Z M 2016 Physica A 453 290
[6] Hu P, Fan W L and Mei S W 2015 Physica A 429 169
[7] Liu J, Xiong Q Y, Shi W R, Shi X and Wang K 2016 Physica A 452 209
[8] Du Y X, Gao C, Hu Y, Mahadevanc S and Deng Y 2014 Physica A 399 57
[9] Liu Z H, Jiang C, Wang J Y and Yu H 2015 Knowledge-Based Systems 84 56
[10] Yang Y Y and Xie G 2016 Inform. Process. Manag. 52 911
[11] Hu J T, Du Y X, Mo H M, Wei D J and Deng Y 2016 Physica A 444 73
[12] Xu Y J, Gao Z, Xiao B, Meng F Y and Lin Z Q 2013 Proc. IEEE International Conference on Broadband Network & Multimedia Technology, November 17-19, 2013, Guilin, China, p. 105
[13] Wang P, Yu X H, Lv J H and Chen A M 2014 Proc. IEEE International Symposium on Circuits and Systems, June 1-5, 2014, Melbourne, Australia, p. 1267
[14] Hu F, Liu Y H and Jin J Z 2014 Proc. International Symposium on Distributed Computing and Applications to Business, Engineering and Science, November 24-27, 2014, Xianning, China, p. 138
[15] Ventresca M, Harrison K R and Ombuki-Berman B M 2015 Lecture Notes in Computer Science 9028 164
[16] Xiao Q 2016 Tehnički Vjesnik 23 397
[17] Metropolis N, Rosenbluth A W, Rosenbluth M N, Teller A H and Teller E 1953 J. Chem. Phys. 21 1087
[18] Burke E K and Kendall G 2005 Search methodologies: introductory tutorials in optimization and decision support techniques (New York: Springer) p. 187
[19] Pablo G and Leon D 2003 Advances in Complex Systems 6 565
[20] Han C F and Liu L 2009 Proc. IEEE International Conference on Engineering Management and Service Science, September 20-22, 2009, Wuhan, China, p. 2
[21] Hidefumi S 2012 Proc. IEEE World Congress on Computational Intelligence, June 10-15, 2012, Brisbane, Australia, p. 1
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