| INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
Prev
|
|
|
Detection of influential nodes with multi-scale information |
| Jing-En Wang(王静恩)1,†, San-Yang Liu(刘三阳)1, Ahmed Aljmiai2, and Yi-Guang Bai(白艺光)1,‡ |
1 School of Mathematics and Statistics, Xidian University, Xi'an 710071, China;
2 David R. Cheriton School of Computer Science, University of Waterloo, Canada |
|
|
|
|
Abstract The identification of influential nodes in complex networks is one of the most exciting topics in network science. The latest work successfully compares each node using local connectivity and weak tie theory from a new perspective. We study the structural properties of networks in depth and extend this successful node evaluation from single-scale to multi-scale. In particular, one novel position parameter based on node transmission efficiency is proposed, which mainly depends on the shortest distances from target nodes to high-degree nodes. In this regard, the novel multi-scale information importance (MSII) method is proposed to better identify the crucial nodes by combining the network's local connectivity and global position information. In simulation comparisons, five state-of-the-art algorithms, i.e. the neighbor nodes degree algorithm (NND), betweenness centrality, closeness centrality, Katz centrality and the k-shell decomposition method, are selected to compare with our MSII. The results demonstrate that our method obtains superior performance in terms of robustness and spreading propagation for both real-world and artificial networks.
|
Received: 30 March 2021
Revised: 25 April 2021
Accepted manuscript online: 08 May 2021
|
|
PACS:
|
89.75.Fb
|
(Structures and organization in complex systems)
|
|
| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11801430, 11801200, 61877046, and 61877047). |
Corresponding Authors:
Jing-En Wang, Yi-Guang Bai
E-mail: jewang008@126.com;ygbai@foxmail.com
|
Cite this article:
Jing-En Wang(王静恩), San-Yang Liu(刘三阳), Ahmed Aljmiai, and Yi-Guang Bai(白艺光) Detection of influential nodes with multi-scale information 2021 Chin. Phys. B 30 088902
|
[1] Fang X, Misra S, Xue G and D Yang 2012 IEEE Commun. Surv. Tut. 14 944
[2] Watts D J, Dodds P S and Newman M E J 2002 Science 1302 296
[3] Tang L, Jing K, He J and Stanley H 2016 Physica A 58 443
[4] Tang L, Jing K, He J and Stanley H 2016 Physica A 129 459
[5] Xu X L, Liu C P and He D R 2016 Chin. Phys. Lett. 33 48901
[6] Yang Y Z, Hu M and Huang T Y 2020 Chin. Phys. B29 088903
[7] He J L and Chen D B, Sun C J, et al. 2017 Physica A 438 469
[8] Wang W, Tang M, Stanley E H, et al. 2017 Rep. Prog. Phys. 80 036603
[9] Liu J G, Hou L, Pan X, et al. 2016 Sci Rep-UK 6 18653
[10] Li G J, Hu J, Song Y Q, et al. 2012 Sci Rep-UK 2 292
[11] Langville A N and Meyer C D 2011 Math. Intell. 30 68
[12] Bartesaghi P, Benzi M, Clemente G P, et al. 2020 Siam. J. Financ. Math. 11 526
[13] Bai Y, Liu S, Yin K, et al. 2019 Appl. Math. Model 75 333
[14] Gong Y D, Liu S Y and Bai Y G 2021 Knowledge-Based Systems 0950 106942
[15] Bai Y G, liu S Y, Li Q and Yuan J 2021 IEEE Trans. Syst., Man, Cybern., Syst. 1
[16] Wang X J, Song M, Jin L, et al. 2017 Chin. Phys. B 26 088901
[17] Song W J, Liu S Y and Bai Y G 2019 Int. J. Mod. Phys. C 30 1950082
[18] Liu J 2021 Chin. Phys. B 30 016401
[19] Bonacich P 2019 J. Math. Sociol. 2 113
[20] Wang J W, Rong L L and Guo T Z 2010J. Dalian Univ. Technol 50 822
[21] Sabidussi G 1996 Psychometrika 31 581
[22] K-I G, Oh E, Kahng B, et al. 2003 Phys. Rev. E 67 017101
[23] Bai Y G, Liu S Y and Zhang Z H 2017 Int. J. Mod. Phys. C 28 1750107
[24] Katz L 1953 Phychometrika 18 39
[25] Lü L, Zhou T, Zhang Q M, et al. 2016 Nat. Commun. 7 10168
[26] Bonacich P 1999 J. Pers. Soc. Psychol 77 967
[27] Kitsak M, Gallos L K, Havlin S, et al. 2010 Nat. Phys. 6 888
[28] Huang L Y, Tang P C, Huo Y L et al. 2019 Acta Phys. Sin. 68 128901 (in Chinese)
[29] Yang Y Z, Hu M and Huang T Y 2020 Chin. Phys. B 29 088903
[30] Lü L, Zhou T, Zhang Q M, et al. 2016 Nat. Commun. 7 10168
[31] Ruan Y R, Lao S Y, Xiao Y D, et al. 2016 Chin. Phys. Lett. 33 28901
[32] Liu F, Wang Z and Y Deng 2020 Knowledge-Based Systems 193 105464
[33] Ruan Y, Tang Y, Hu Y, et al. 2020 IEEE Access 8 2972107
[34] Yang H H and An S 2020 Symmetry 12 123
[35] Sun Q, Yang G and Zhou A 2020 Complexity 2020 1
[36] Yang Y Z, Yu L, Wang X, et al. 2020 Int. J. Mod. Phys. C 31 2050022
[37] Yang Y Z, Hu M and Huang T Y 2020 Chin. Phys. B 29 088903
[38] Granovetter M S 1973 Amer. J. Sociol. 78 1360
[39] Onnela J P, Saramaeki J, Hyvoenen J, et al. 2007 Proc. Nat. Acad. Sci. USA 104 7332
[40] Salton G and Mcgill M J 1983 New Zealand: McGraw-Hill 32 373
[41] Meng Y, Chen G and Fu X 2011 Physica A:Statal Mechanics and its Applications 390 2408
[42] Cheng X, Ren F, Shen H, et al. 2010 Journal of Statal Mechanics: Theory and Experiment 10 P10011
[43] Zachary W W 1977J. Anthropol. Res. 33 452
[44] Lusseau D, Schneider K, Boisseau O J, et al. 2003 Behav. Ecol. Sociobiol 54 396
[45] Watts D J and Strogatz S H 1998 Nature 393 6684
[46] Kendall M 1938 Biometrika 30 81 |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|
