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Identifying influential nodes based on graph signal processing in complex networks |
Zhao Jia (赵佳), Yu Li (喻莉), Li Jing-Ru (李静茹), Zhou Peng (周鹏) |
Department of Electronics and Information Engineering, Huazhong University of Science and technology, Wuhan 430074, China |
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Abstract Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity (i.e., different attributes such as interest, energy, age, and so on) ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal processing based centrality (GSPC) method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.
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Received: 28 July 2014
Revised: 09 December 2014
Accepted manuscript online:
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PACS:
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89.75.-k
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(Complex systems)
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02.30.Nw
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(Fourier analysis)
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02.70.Hm
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(Spectral methods)
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Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61231010) and the Fundamental Research Funds for the Central Universities, China (Grant No. HUST No. 2012QN076). |
Corresponding Authors:
Yu Li
E-mail: hustlyu@hust.edu.cn
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About author: 89.75.-k; 02.30.Nw; 02.70.Hm |
Cite this article:
Zhao Jia (赵佳), Yu Li (喻莉), Li Jing-Ru (李静茹), Zhou Peng (周鹏) Identifying influential nodes based on graph signal processing in complex networks 2015 Chin. Phys. B 24 058904
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[1] |
Barabási A L 2007 IEEE Control System Magazine 27 33
|
[2] |
Yeung C H and Saad D 2013 J. Phys. A: Math. Theor. 46 103001
|
[3] |
Lu Y L, Jiang G P and Song Y R 2012 Chin. Phys. B 21 100207
|
[4] |
Wu Y, Hu Y, He X H and Deng K 2014 Chin. Phys. B 23 060101
|
[5] |
Fakhteh G and Konstantin K 2012 Europhys. Lett. 99 58006
|
[6] |
Halu A, Zhao K, Baronchelli A and Bianconi G 2013 Europhys. Lett. 102 16002
|
[7] |
Chen S M, Pang S P and Zou X Q 2013 Chin. Phys. B 22 058901
|
[8] |
Leskovec J, Adamic L A and Huberman B A 2007 ACM Transactions on the Web 1 5
|
[9] |
Goldenberg J, Han S, Lehmann D R and Hong J W 2009 Journal of Marketing 73 2
|
[10] |
Sabidussi G 1966 Psychometrika 31 581
|
[11] |
Freeman L C 1979 Social Networks 1 215
|
[12] |
Chen D B, Lü L Y, Shang M S, Zhang Y C and Zhou T 2012 Physica A 391 1777
|
[13] |
Bonacich P 2007 Social Networks 29 555
|
[14] |
Page L, Brin S, Motwani R and Winograd T 1999 Stanford InfoLab
|
[15] |
Lü L Y, Zhang Y C, Yeung C H and Zhou T 2011 PLoS ONE 6 e21202
|
[16] |
Ilyas M U, Shafiq M Z, Liu A X and Radha H 2011 INFOCOM, 2011 Proceedings IEEE p. 561
|
[17] |
Liang N C, Chen P C, Sun T, Chen L J and Mario G 2006 Systems, Man and Cybernetics 2006 IEEE International Conference on p. 187
|
[18] |
Katiyar V, Chand N and Soni S 2011 International Journal of Advanced Networking and Applications 2 4
|
[19] |
Yang Z C and John C S L 2011 ACM SIGMETRICS Performance Evaluation Review 39 52
|
[20] |
Watts D J and Strogatz S H 1998 Nature 393 440
|
[21] |
Theodorakopoulos G and Baras J S 2006 IEEE Journal on Selected Areas in Communications 24 318
|
[22] |
Cha M, Haddadi H, Benevenuto F and Krishna P 2010 in ICWSM'10: Proceedings of international AAAI Conference on Weblogs and Social Media p. 10
|
[23] |
Parantapa B, Muhammad B Z, Niloy G, Saptarshi G and Krishna G 2014 ACM Recommender System Conference to appear
|
[24] |
Malcolm G 2000 The tipping point: How Little things can make a big difference pp. 33-41
|
[25] |
Wu S, J M H, Mason W A and Watts D J 2011 in Proc 20th Intl Conf WWW pp. 705-714
|
[26] |
Shuman D I, Narang S K, Frossard P, Ortega A and Vandergheynst P 2013 Signal Processing Magazine, IEEE 30 83
|
[27] |
Sandryhaila A and Moura J 2013 Signal Processing, IEEE Transactions on 61 1644
|
[28] |
Bonacich P 1987 American Journal of Sociology 1170
|
[29] |
Zachary W 1977 Journal of Anthropological Research 33 452
|
[30] |
Krackhardt D 1987 Social Networks 9 109
|
[31] |
Barrat A, Barthelemy M and Vespignani A 2008 Dynamical Processes on Complex Networks (Cambridge: Cambridge University Press)
|
[32] |
Zhou T, Liu J G, Bai W J, Chen G R and Wang B H 2006 Phys. Rev. E 74 056109
|
[33] |
Barabási A L and Albert R 1999 Science 286 509
|
[34] |
Dorogovtsev S N and Menders J F F 2000 Phys. Rev. E 62 1842
|
[35] |
Opsahl T 2010 Social network 35 159
|
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