Skip to main content
Log in

Identifying Influential Spreaders in Complex Networks by Considering the Impact of the Number of Shortest Paths

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

Abstract

The study on how to identify influential spreaders in complex networks is becoming increasingly significant. Previous studies demonstrate that considering the shortest path length can improve the accuracy of identification, but which ignore the influence of the number of shortest paths. In many cases, even though the shortest path length of two nodes is rather larger, their interaction influence is also significant if the number of shortest paths between them is considerable. Inspired by this fact, the authors propose an improved centrality index (ICC) based on well-known closeness centrality and a semi-local iterative algorithm (semi-IA) to study the impact of the number of shortest paths on the identification of the influential spreaders. By comparing with several traditional centrality indices, such as degree centrality, k-shell decomposition, betweenness centrality and eigenvector centrality, the experimental results on real networks indicate that the ICC index and semi-IA have a better performance, regardless of the identification capability or the resolution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Basaras P, Katsaros D, and Tassiulas L, Detecting influential spreaders in complex, dynamic networks, Computer, 2013, 46(4): 24–29.

    Article  Google Scholar 

  2. Lü L, Zhang Y C, Yeung C H, et al., Leaders in social networks, the delicious case, PLoS One, 2011, 6(6): e21202.

    Article  Google Scholar 

  3. Borge-Holthoefer J and Moreno Y, Absence of influential spreaders in rumor dynamics, Physical Review E, 2012, 85(2): 026116.

    Article  Google Scholar 

  4. Wang P, Tian C, and Lu J, Identifying influential spreaders in artificial complex networks, Journal of Systems Science and Complexity, 2014, 27(4): 650–665.

    Article  Google Scholar 

  5. Chen D B, Xiao R, Zeng A, et al., Path diversity improves the identification of influential spreaders, EPL (Europhysics Letters), 2013, 104(6): 68006.

    Article  Google Scholar 

  6. Ren Z M, Zeng A, Chen D B, et al., Iterative resource allocation for ranking spreaders in complex networks, EPL (Europhysics Letters), 2014, 106(4): 48805.

    Article  Google Scholar 

  7. Ahmad Z and Amir S, A hierarchical approach for influential node ranking in complex social networks, Expert Systems with Applications, 2018, 93: 200–211.

    Article  Google Scholar 

  8. Sotoodeh H and Falahrad M, Relative degree structural hole centrality, CRD-SH: A new centrality measure in complex networks, Journal of Systems Science and Complexity, 2019, 32(5): 1306–1323.

    Article  MathSciNet  Google Scholar 

  9. Tang J, Zhang R, Wang P, et al., A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks, Knowledge-Based Systems, 2020, 187: 104833.

    Article  Google Scholar 

  10. Bonacich P, Factoring and weighting approaches to status scores and clique identification, Journal of Mathematical Sociology, 1972, 2(1): 113–120.

    Article  Google Scholar 

  11. Kitsak M, Gallos L K, Havlin S, et al. A, Identification of influential spreaders in complex networks, Nature Physics, 2010, 6(11): 888–893.

    Article  Google Scholar 

  12. Freeman L C, A set of measures of centrality based on betweenness, Sociometry, 1977, 40(1): 35–41.

    Article  Google Scholar 

  13. Bonacich P, Power and centrality: A family of measures, Journal of Mathematical Sociology, 1987, 92(1): 1170–1182.

    Google Scholar 

  14. Zeng A and Zhang C J, Ranking spreaders by decomposing complex networks, Physics Letters A, 2013, 377(14): 1031–1035.

    Article  Google Scholar 

  15. Sabidussi G, The centrality index of a graph, Psychometrika, 1966, 31(4): 581–603.

    Article  MathSciNet  Google Scholar 

  16. Ma L L, Ma C, Zhang H F, et al., Identifying influential spreaders in complex networks based on gravity formula, Physica A, 2016, 451: 205–212.

    Article  Google Scholar 

  17. Liu H L, Ma C, Xiang B B, et al., Identifying multiple influential spreaders based on generalized closeness centrality, Physica A, 2018, 492(1): 2237–2248.

    Article  Google Scholar 

  18. Bao Z K, Ma C, Xiang B B, et al., Identification of influential nodes in complex networks: Method from spreading probability viewpoint, Physica A, 2017, 468: 391–397.

    Article  Google Scholar 

  19. Benzi M, Estrada E, and Klymko C, Ranking hubs and authorities using matrix functions, Linear Algebra and Its Applications, 2013, 438(5): 2447–2474.

    Article  MathSciNet  Google Scholar 

  20. Liu Y, Tang M, Zhou T, et al., Core-like groups result in invalidation of identifying superspreader by k-shell decomposition, Scientific Reports, 2015, 5: 9602.

    Article  Google Scholar 

  21. Newman M E, Finding community structure in networks using the eigenvectors of matrices, Physical Review E, 2006, 74(3): 036104.

    Article  MathSciNet  Google Scholar 

  22. Watts D J and Strogatz S H, Collective dynamics of small-worldnetworks, Nature, 1998, 393(6684): 440–442.

    Article  Google Scholar 

  23. Guimera R, Danon L, Diaz-Guilera A, et al., Self-similar community structure in a network of human interactions, Physical Review E, 2003, 68(6): 065103.

    Article  Google Scholar 

  24. Blagus N, Subelj L, Bajec M, Self-similar scaling of density in complex real-world networks, Physica A, 2012, 391(8): 2794–2802.

    Article  Google Scholar 

  25. Duch J and Arenas A, Community detection in complex networks using extremal optimization, Physical Review E, 2005, 72: 027104.

    Article  Google Scholar 

  26. Mering C V, Krause R, Snel B, et al., Comparative assessment of large-scale data sets of proteinprotein interactions, Nature, 2002, 417: 399–403.

    Article  Google Scholar 

  27. Spring N, Mahajan R, Wetherall D, et al., Measuring ISP topologies with rocketfuel, IEEE/ACM Transactions on Networking, 2004, 12(1): 2–16.

    Article  Google Scholar 

  28. Leskovec J, Kleinberg J, and Faloutsos C, Graph evolution: Densification and shrinking diameters, ACM Transactions on Knowledge Discovery from Data (TKDD), 2007, 1(1): 2–42.

    Article  Google Scholar 

  29. Moreno Y, Pastor-Satorras R, and Vespignani A, Epidemic outbreaks in complex heterogeneous networks, The European Physical Journal B, 2002, 26(4): 521–529.

    Google Scholar 

  30. Bae J and Kim S, Identifying and ranking influential spreaders in complex networks by neighborhood coreness, Physica A, 2014, 395: 549–559.

    Article  MathSciNet  Google Scholar 

  31. Knight W R, A computer method for calculating kendall’s tau with ungrouped data, Journal of the American Statistical Association, 1966, 61(314): 436–439.

    Article  Google Scholar 

  32. Zhou T, Lü L, and Zhang Y C, Predicting missing links via local information, The European Physical Journal B, 2009, 71(4): 623–630.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhongkui Bao.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant Nos. 61973001, 61806001, the Natural Science Foundation of Anhui Province under Grant No. 1808085MF201, the State Key Laboratory for Ocean Big Data Mining and Application of Zhejiang Province under Grant No. OBDMA201502, and Anhui University Foundation under Grant No. 01005102.

This paper was recommended for publication by Editor LIU Guoping.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luan, Y., Bao, Z. & Zhang, H. Identifying Influential Spreaders in Complex Networks by Considering the Impact of the Number of Shortest Paths. J Syst Sci Complex 34, 2168–2181 (2021). https://doi.org/10.1007/s11424-021-0111-7

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11424-021-0111-7

Keywords

Navigation