Please wait a minute...
Chin. Phys. B, 2020, Vol. 29(4): 048902    DOI: 10.1088/1674-1056/ab77fe
INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY Prev  

Identifying influential spreaders in complex networks based on entropy weight method and gravity law

Xiao-Li Yan(闫小丽)1,2,3, Ya-Peng Cui(崔亚鹏)1,2,3, Shun-Jiang Ni(倪顺江)1,2,3
1 Institute of Public Safety Research, Tsinghua University, Beijing 100084, China;
2 Department of Engineering Physics, Tsinghua University, Beijing 100084, China;
3 Beijing Key Laboratory of City Integrated Emergency Response Science, Beijing 100084, China
Abstract  In complex networks, identifying influential spreader is of great significance for improving the reliability of networks and ensuring the safe and effective operation of networks. Nowadays, it is widely used in power networks, aviation networks, computer networks, and social networks, and so on. Traditional centrality methods mainly include degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, k-shell, etc. However, single centrality method is one-sided and inaccurate, and sometimes many nodes have the same centrality value, namely the same ranking result, which makes it difficult to distinguish between nodes. According to several classical methods of identifying influential nodes, in this paper we propose a novel method that is more full-scaled and universally applicable. Taken into account in this method are several aspects of node's properties, including local topological characteristics, central location of nodes, propagation characteristics, and properties of neighbor nodes. In view of the idea of the multi-attribute decision-making, we regard the basic centrality method as node's attribute and use the entropy weight method to weigh different attributes, and obtain node's combined centrality. Then, the combined centrality is applied to the gravity law to comprehensively identify influential nodes in networks. Finally, the classical susceptible-infected-recovered (SIR) model is used to simulate the epidemic spreading in six real-society networks. Our proposed method not only considers the four topological properties of nodes, but also emphasizes the influence of neighbor nodes from the aspect of gravity. It is proved that the new method can effectively overcome the disadvantages of single centrality method and increase the accuracy of identifying influential nodes, which is of great significance for monitoring and controlling the complex networks.
Keywords:  complex networks      influential nodes      entropy weight method      gravity law  
Received:  21 November 2019      Revised:  30 January 2020      Published:  05 April 2020
PACS:  89.75.Hc (Networks and genealogical trees)  
Fund: Project support by the National Key Research and Development Program of China (Grant No. 2018YFF0301000) and the National Natural Science Foundation of China (Grant Nos. 71673161 and 71790613).
Corresponding Authors:  Shun-Jiang Ni     E-mail:  sjni@tsinghua.edu.cn

Cite this article: 

Xiao-Li Yan(闫小丽), Ya-Peng Cui(崔亚鹏), Shun-Jiang Ni(倪顺江) Identifying influential spreaders in complex networks based on entropy weight method and gravity law 2020 Chin. Phys. B 29 048902

[1] Zanin M and Lillo F 2013 Eur. Phys. J. Spec. Top. 215 5
[2] Lordan O, Sallan J M and Simo P 2014 J. Transp. Geogr. 37 112
[3] Li H J, Li H Y and Jia C L 2015 Int. J. Mod. Phys. C 26 1550043
[4] Arularasan A N, Suresh A and Seerangan K 2019 Cluster Comput. 22 4035
[5] Shang Y L 2015 J. Syst. Sci. Complexity 28 96
[6] Ma L L, Ma C, Zhang H F and Wang B H 2016 Physica A 451 205
[7] Kitsak M, Gallos L K, Havlin S, Liljeros F, Muchnik L, Stanley H E and Makse H A 2010 Nat. Phys. 6 888
[8] Bae J and Kim S 2014 Phys. A Stat. Mech. Appl. 395 549
[9] Lü L Y, Zhang Y C, Yeung C H and Zhou T 2011 PLoS ONE 6 e21202
[10] Chen D B, Lü L Y, Shang M S, Zhang Y C and Zhou T 2012 Physica A 391 1777
[11] Lü L Y, Chen D B and Zhou T 2011 New J. Phys. 13 123005
[12] Mehta A and Gupta R 2015 arXiv:1509.07966v1 [cs.SI]
[13] Wang X Y, Wang Y, Qin X M, Li R and Eustace J 2018 Chin. Phys. B 27 100504
[14] Fei L G and Deng Y 2017 Chaos, Solitons and Fractals 104 257
[15] Kang L, Xiang B B, Zhai S L, Bao Z K and Zhang H F 2018 Acta Phys. Sin. 67 198901 (in Chinese)
[16] Freeman L C 1978 Soc. Netw. 1 215
[17] Freeman L C 1977 Sociometry 40 35
[18] Bonacich P and Lloyd P 2001 Soc. Netw. 23 191
[19] Lü L Y, Chen D B, Ren X L, Zhang Q M, Zhang Y C and Zhou T 2016 Phys. Rep. 650 1
[20] Wen T and Deng Y 2020 Inform. Sci. 512 549
[21] Fei L G, Zhang Q and Deng Y 2018 Physica A 512 1044
[22] Gao S, Ma J, Chen Z M, Wang G H and Xing C M 2014 Physica A 403 130
[23] Zhong L F, Liu J G and Shang M S 2015 Phys. Lett. A 379 2272
[24] Wang Y C, Wang S S and Deng Y 2019 Pramana-J. Phys. 92 68
[25] Zeng A and Zhang C J 2013 Phys. Lett. A 377 1031
[26] Song B, Jiang G P, Song Y R and Xia L L 2015 Chin. Phys. B 24 100101
[27] Yin R R, Yin X L, Cui M D and Xu Y H 2019 J. Wireless Com. Network 2019 234
[28] Fei L G, Mo H M and Deng Y 2017 Mod. Phys. Lett. B 31 1750243
[29] Du Y X, Gao C, Hu Y, Mahadevan S and Deng Y 2014 Physica A 399 57
[30] Liu Y J, Wu J and Liang C Y 2015 Kybernetes 44 1437
[31] Mo H M and Deng Y 2019 Physica A 529 121538
[32] Bian T, Hu J T and Deng Y 2017 Physica A 479 422
[33] Hu J T, Du Y X, Mo H M, Wei D J and Deng Yong 2016 Physica A 444 73
[34] Li Z, Ren T, Ma X Q, Liu S M, Zhang Y X and Zhou T 2019 Sci. Rep. 9 8387
[35] Ibnoulouafi A and Haziti M E 2018 Chaos, Solitons and Fractals 114 69
[36] Kermack W O and McKendrick A G 1927 Proc. R. Soc. Lond. A 115 700
[37] http://konect.uni-koblenz.de/networks/moreno_health
[38] http://konect.uni-koblenz.de/networks/advogato
[39] https://icon.colorado.edu/#!/networks
[40] http://vlado.fmf.uni-lj.si/pub/networks/data/collab/geom.htm
[41] http://konect.uni-koblenz.de/networks/opsahl-usairport
[42] http://networkrepository.com/bio-CE-GN.php
[43] Li C, Wang L, Sun S W and Xia C Y 2018 Appl. Math. Comput. 320 512
[44] Knight W R 1966 J. Amer. Statist. Assoc. 61 436
[45] Kendall M G 1938 Biometrika 30 81
[46] Kendall M G 1945 Biometrika 33 239
[47] Ruan Y R, Lao S Y, Xiao Y D, Wang J D and Bai L 2016 Chin. Phys. Lett. 33 028901
[48] Wang J Y, Hou X N, Li K Z and Ding Y 2017 Physica A 475 88
[49] Liu Y, Tang M, Zhou T and Do Y H 2015 Sci. Rep. 5 9602
[1] Influential nodes identification in complex networks based on global and local information
Yuan-Zhi Yang(杨远志), Min Hu(胡敏), Tai-Yu Huang(黄泰愚). Chin. Phys. B, 2020, 29(8): 088903.
[2] Modeling and analysis of the ocean dynamic with Gaussian complex network
Xin Sun(孙鑫), Yongbo Yu(于勇波), Yuting Yang(杨玉婷), Junyu Dong(董军宇), Christian Böhm(陈学恩), Xueen Chen. Chin. Phys. B, 2020, 29(10): 108901.
[3] Pyramid scheme model for consumption rebate frauds
Yong Shi(石勇), Bo Li(李博), Wen Long(龙文). Chin. Phys. B, 2019, 28(7): 078901.
[4] Theoretical analyses of stock correlations affected by subprime crisis and total assets: Network properties and corresponding physical mechanisms
Shi-Zhao Zhu(朱世钊), Yu-Qing Wang(王玉青), Bing-Hong Wang(汪秉宏). Chin. Phys. B, 2019, 28(10): 108901.
[5] Coordinated chaos control of urban expressway based on synchronization of complex networks
Ming-bao Pang(庞明宝), Yu-man Huang(黄玉满). Chin. Phys. B, 2018, 27(11): 118902.
[6] Detecting overlapping communities based on vital nodes in complex networks
Xingyuan Wang(王兴元), Yu Wang(王宇), Xiaomeng Qin(秦小蒙), Rui Li(李睿), Justine Eustace. Chin. Phys. B, 2018, 27(10): 100504.
[7] Dominant phase-advanced driving analysis of self-sustained oscillations in biological networks
Zhi-gang Zheng(郑志刚), Yu Qian(钱郁). Chin. Phys. B, 2018, 27(1): 018901.
[8] Ranking important nodes in complex networks by simulated annealing
Yu Sun(孙昱), Pei-Yang Yao(姚佩阳), Lu-Jun Wan(万路军), Jian Shen(申健), Yun Zhong(钟赟). Chin. Phys. B, 2017, 26(2): 020201.
[9] Empirical topological investigation of practical supply chains based on complex networks
Hao Liao(廖好), Jing Shen(沈婧), Xing-Tong Wu(吴兴桐), Bo-Kui Chen(陈博奎), Mingyang Zhou(周明洋). Chin. Phys. B, 2017, 26(11): 110505.
[10] An improved genetic algorithm with dynamic topology
Kai-Quan Cai(蔡开泉), Yan-Wu Tang(唐焱武), Xue-Jun Zhang(张学军), Xiang-Min Guan(管祥民). Chin. Phys. B, 2016, 25(12): 128904.
[11] Subtle role of latency for information diffusion in online social networks
Fei Xiong(熊菲), Xi-Meng Wang(王夕萌), Jun-Jun Cheng(程军军). Chin. Phys. B, 2016, 25(10): 108904.
[12] Synchronization of Markovian jumping complex networks with event-triggered control
Shao Hao-Yu, Hu Ai-Hua, Liu Dan. Chin. Phys. B, 2015, 24(9): 098902.
[13] Load-redistribution strategy based on time-varying load against cascading failure of complex network
Liu Jun, Xiong Qing-Yu, Shi Xin, Wang Kai, Shi Wei-Ren. Chin. Phys. B, 2015, 24(7): 076401.
[14] Degree distribution and robustness of cooperativecommunication network with scale-free model
Wang Jian-Rong, Wang Jian-Ping, He Zhen, Xu Hai-Tao. Chin. Phys. B, 2015, 24(6): 060101.
[15] Identifying influential nodes based on graph signal processing in complex networks
Zhao Jia, Yu Li, Li Jing-Ru, Zhou Peng. Chin. Phys. B, 2015, 24(5): 058904.
No Suggested Reading articles found!