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A new metric to quantify influence of nodes in social networks

College of Mathematics and Informatics, Fujian Normal University, Fuzhou, Fujian 350117, P. R. China

Fujian Provincial Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou, Fujian 350007, P. R. China

E-mail Address: zhoushuming@fjnu.edu.cn

Corresponding author.

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Jiafei Liu

College of Mathematics and Informatics, Fujian Normal University, Fuzhou, Fujian 350117, P. R. China

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Gaolin Chen

College of Mathematics and Informatics, Fujian Normal University, Fuzhou, Fujian 350117, P. R. China

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Zhendong Gu

College of Mathematics and Informatics, Fujian Normal University, Fuzhou, Fujian 350117, P. R. China

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, and

Yihong Wang

College of Mathematics and Informatics, Fujian Normal University, Fuzhou, Fujian 350117, P. R. China

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https://doi.org/10.1142/S0217979219501868Cited by:2 (Source: Crossref)

Abstract

Reasonably ranking the influence of nodes in social networks is increasingly important not only for theoretical research but also for real applications. A great number of strategies to identify the influence of nodes have been proposed so far, such as semi-local centrality (SL), betweenness centrality and coreness centrality, etc. For the sake of ranking more effectively, a new method of identifying influential nodes is proposed in this paper, which takes into account a node’s influence on its neighbors and the node’s position in the network. The influence on neighbors involves two aspects. One is the influence of the target node on its direct neighbors (h-index), the other is the influence on farther neighbors (semi-local centrality). The location of the node in the network is reflected by the improved k-core score, a modified version of k-core index to make it more applicable to practice. Combining both local and global information of node together makes the proposed method a reasonable and effective strategy to identify the influential nodes. The simulation results compared to other well-known methods on six real-world networks demonstrate the effectiveness of the presented method.

Keywords:

PACS: 05.40.Fb, 05.90.+m

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References

1. L. Adamic and N. Glance, The political blogosphere and the 2004 US Election, Int. Workshop Link Discovery, (2005). Google Scholar
2. R. M. Anderson, R. M. May and B. Anderson, Infectious Diseases of Humans: Dynamics and Control (Oxford University Press, Oxford, 1992). Google Scholar
3. D.-B. Chen et al., Physica A 391, 1777 (2012). Web of Science, ADS, Google Scholar
4. J.-C. Delvenne, R. Lambiotte and L. E. C. Rocha, Nat. Commun. 6, 7366 (2015). Web of Science, ADS, Google Scholar
5. S. N. Dorogovtsev, A. V. Goltsev and J. F. F. Mendes, Phys. Rev. Lett. 96, 040601 (2006). Web of Science, ADS, Google Scholar
6. J. H. Fewell, Sci. 301, 1867 (2003). Web of Science, ADS, Google Scholar
7. S. Fortunato, Phys. Rep. 486, 75 (2010). Web of Science, ADS, Google Scholar
8. L. C. Freeman, Social Netw. 1, 215 (1979). Web of Science, Google Scholar
9. L. C. Freeman, Sociometry 40, 35 (1997). Google Scholar
10. N. Friedkin, Am. J. Sociol. 96, 1478 (1991). Web of Science, Google Scholar
11. R. Guimerà et al., Phys. Rev. Lett. 89, 248701 (2002). Web of Science, ADS, Google Scholar
12. W. Hoeffding, Ann. Math. Statist. 19, 546 (1994). Google Scholar
13. J. Hu et al., Phys. A 444, 73 (2016). Web of Science, ADS, Google Scholar
14. A. Ilany and E. Akçay, Nat. Commun. 7, 12084 (2016). Web of Science, ADS, Google Scholar
15. M. T. Irfan and L. E. Ortiz, Artif. Intell. 215, 79 (2014). Web of Science, Google Scholar
16. M. G. Kendall, Biometrika 33, (1945). Google Scholar
17. M. Kimura et al., Int. J. Data Sci. Anal. 1, 3 (2016). Google Scholar
18. M. Kitsak et al., Nat. Phys. 6, 888 (2010). Web of Science, ADS, Google Scholar
19. G. Lawyer, Sci. Rep. 5, 8665 (2015). Web of Science, ADS, Google Scholar
20. X.-J. Li et al., Neurocomputing 210, 141 (2016). Web of Science, Google Scholar
21. D. Li et al., Neurocomputing 260, 69 (2017). Web of Science, Google Scholar
22. Z.-P. Li, Y. Zuo and H.-Y. Wang, Chin. J. Electron. 44, 2967 (2016). Google Scholar
23. D. Liu et al., Sci. Rep. 7, 43330 (2017). Web of Science, ADS, Google Scholar
24. S. Liu et al., Int. J. Mod. Phys. B 31, 1650254 (2017). Link, Web of Science, ADS, Google Scholar
25. D. Lusseau et al., Behav. Ecol. Sociobiol. 54, 396 (2003). Web of Science, Google Scholar
26. J. Lu and W. Wan, ETRI J. 38, 52 (2016). Web of Science, Google Scholar
27. F. Lu et al., J. Netw. Comput. Appl. 86, 15 (2017). Web of Science, Google Scholar
28. L. Lü et al., Phys. Rep. 650, 1 (2016). Web of Science, ADS, Google Scholar
29. L. Lü et al., PLoS One 6, e21202 (2011). Web of Science, ADS, Google Scholar
30. L. Lü et al., Nat. Commun. 7, 10168 (2016). Web of Science, ADS, Google Scholar
31. F. D. Malliaros, M. E. G. Rossi and M. Vazirgiannis, Sci. Rep. 6, 19307 (2016). Web of Science, ADS, Google Scholar
32. M. Newman, SIAM Rev. 45, 167 (2003). Web of Science, ADS, Google Scholar
33. S. Pemmaraju and S. Skiena, in Computational Discrete Mathematics: Combinatorics and Graph Theory in Mathematica (Cambridge University Press, Cambridge, England, 2003), pp. 330–331 and 353–356. Google Scholar
34. S.-C. Peng et al., IEEE Trans. Dependable Secur. Comput. (2017), https://doi.org/ 10.1109/TDSC.2017.2731844. Google Scholar
35. S.-C. Peng et al., Inform. Sci. 379, 146 (2016). Web of Science, Google Scholar
36. X. Qi, R. D. Duval and K. Christensen, Social Netw. 2, 19 (2013). Google Scholar
37. X. Qi et al., Inform. Sci. 194, 240 (2012). Web of Science, Google Scholar
38. M. Ripeanu, I. Foster and A. Iamnitchi, IEEE Internet Comput. 6, 50 (2002). Web of Science, Google Scholar
39. G. Sabidussi, Psychometrika 31, 581 (1966). Web of Science, Google Scholar
40. F. Souam, A. Aïtelhadj and R. Baba-Ali, Knowl. Inf. Syst. 40, 455 (2014) Web of Science, Google Scholar
41. Y.-J. Tan, J. Wu and H.-Z. Deng, Syst. Eng. Theory Pract. 26, 79 (2006). Google Scholar
42. B. Wang et al., IEEE Trans. Knowl. Data Eng. 29, 2168 (2017). Web of Science, Google Scholar
43. Z. Wang et al., Neurocomputing 260, 466 (2017). Web of Science, Google Scholar
44. Z. Wang et al., Phys. A 461, 171 (2016). Web of Science, ADS, Google Scholar
45. D. J. Watts and S. H. Strogatz, Nat. 393, 440 (1998). Web of Science, ADS, Google Scholar
46. H. Wei et al., PLoS One 13, e0200091 (2018). Google Scholar
47. S. Xu and P. Wang, Phys. A 469, 654 (2017). Web of Science, Google Scholar
48. G. Yan et al., Phys. Rev. E: Stat. 73, 046108 (2006). Web of Science, ADS, Google Scholar
49. X. H. Yang et al., Int. J. Mod. Phys. B 30, 1650222 (2016). Link, Web of Science, ADS, Google Scholar
50. F. Yang et al., Phys. Lett. A 382, 2361 (2018). Web of Science, ADS, Google Scholar
51. W. W. Zachary, J. Anthropol. Res. 33, 452 (1977). Web of Science, Google Scholar
52. A. Zareie and A. Sheikhahmadi, Expert Syst. Appl. 93, 200 (2018). Web of Science, Google Scholar
53. X.-H. Zhang et al., Knowl.-Based Syst. 42, 74 (2013). Web of Science, Google Scholar
54. Y.-X. Zhao, S.-H. Li and F. Jin, Neurocomputing 210, 34 (2016). Web of Science, Google Scholar
55. L. F. Zhong, J. G. Liu and M. S. Shang, Phys. Lett. A 379, 2272 (2015). Web of Science, ADS, Google Scholar
56. L. F. Zhong et al., Phys. A 511, 78 (2018). Web of Science, Google Scholar
57. C.-S. Zhu, X.-Y. Wang and L. Zhu, Chaos Solitons Fract. 96, 43 (2017). Web of Science, ADS, Google Scholar
58. Xu. Zhu et al., Int. J. Mod. Phys. B 32, 1850197 (2018). Link, Web of Science, ADS, Google Scholar