Skip to main content
SpringerLink
Log in

A new local and multidimensional ranking measure to detect spreaders in social networks

  • Published:
Computing Aims and scope Submit manuscript

Abstract

Spreaders detection is a vital issue in complex networks because spreaders can spread information to a massive number of nodes in the network. There are many centrality measures to rank nodes based on their ability to spread information. Some local and global centrality measures including DIL, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, PageRank centrality and k-shell decomposition method are used to identify spreader nodes. However, they may have some problems such as finding inappropriate spreaders, unreliable spreader detection, higher time complexity or incompatibility with some networks. In this paper, a new local ranking measure is proposed to identify the influence of a node. The proposed method measures the spreading ability of nodes based on their important location parameters such as node degree, the degree of its neighbors, common links between a node and its neighbors and inverse cluster coefficient. The main advantage of the proposed method is to clear important hubs and low-degree bridges in an efficient manner. To test the efficiency of the proposed method, experiments are conducted on eight real and four synthetic networks. Comparisons based on Susceptible Infected Recovered and Susceptible Infected models reveal that the proposed method outperforms the compared well-known centralities.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Havlin S, Kenett DY, Ben-Jacob E, Bunde A, Cohen R, Hermann H, Kantelhardt J, Kertész J, Kirkpatrick S, Kurths J (2012) Challenges in network science: applications to infrastructures, climate, social systems and economics. Eur Phys J Spec Top 214:273–293

    Article  Google Scholar 

  2. Jia-sheng W, Xiao-ping W, Bo Y, Jiang-wei G (2011) Improved method of node importance evaluation based on node contraction in complex networks. Procedia Eng 15:1600–1604

    Article  Google Scholar 

  3. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D-U (2006) Complex networks: structure and dynamics. Phys Rep 424:175–308

    Article  MathSciNet  Google Scholar 

  4. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442

    Article  Google Scholar 

  5. Newman ME (2002) Assortative mixing in networks. Phys Rev Lett 89:208701

    Article  Google Scholar 

  6. Barabási A-L (2009) Scale-free networks: a decade and beyond. Science 325:412–413

    Article  MathSciNet  Google Scholar 

  7. Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512

    Article  MathSciNet  Google Scholar 

  8. Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:026113

    Article  Google Scholar 

  9. Pei S, Muchnik L, Andrade JS Jr, Zheng Z, Makse HA (2014) Searching for superspreaders of information in real-world social media. arXiv preprint arXiv:1405.1790

  10. Castellano C, Fortunato S, Loreto V (2009) Statistical physics of social dynamics. Rev Mod Phys 81:591

    Article  Google Scholar 

  11. Pei S, Makse HA (2013) Spreading dynamics in complex networks. J Stat Mech Theory Exp 2013:P12002

    Article  Google Scholar 

  12. Borgatti SP (2005) Centrality and network flow. Soc Netw 27:55–71

    Article  Google Scholar 

  13. Borgatti SP, Everett MG (2006) A graph-theoretic perspective on centrality. Soc Netw 28:466–484

    Article  Google Scholar 

  14. Scripps J, Tan PN, Esfahanian AH (2007) Node roles and community structure in networks. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, ACM, 2007, pp 26–35

  15. Lü L, Chen D, Ren X-L, Zhang Q-M, Zhang Y-C, Zhou T (2016) Vital nodes identification in complex networks. Phys Rep 650:1–63

    Article  MathSciNet  Google Scholar 

  16. Berahmand K, Samadi N, Sheikholeslami SM (2018) Effect of rich-club on diffusion in complex networks. Int J Mod Phys B 32:1850142

    Article  Google Scholar 

  17. Chen D, Lü L, Shang M-S, Zhang Y-C, Zhou T (2012) Identifying influential nodes in complex networks. Phys A 391:1777–1787

    Article  Google Scholar 

  18. Gao S, Ma J, Chen Z, Wang G, Xing C (2014) Ranking the spreading ability of nodes in complex networks based on local structure. Phys A 403:130–147

    Article  Google Scholar 

  19. Berahmand K, Bouyer A, Samadi N (2018) A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks. Chaos Solitons Fractals 110:41–54

    Article  Google Scholar 

  20. Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, Makse HA (2010) Identification of influential spreaders in complex networks. Nat Phys 6:888–893

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  22. Liu J-G, Ren Z-M, Guo Q (2013) Ranking the spreading influence in complex networks. Phys A 392:4154–4159

    Article  Google Scholar 

  23. Yeruva S, Devi T, Reddy YS (2016) Selection of influential spreaders in complex networks using Pareto Shell decomposition. Phys A 452:133–144

    Article  Google Scholar 

  24. Zeng A, Zhang C-J (2013) Ranking spreaders by decomposing complex networks. Phys Lett A 377:1031–1035

    Article  Google Scholar 

  25. Liu Y, Tang M, Zhou T, Do Y (2015) Improving the accuracy of the k-shell method by removing redundant links-from a perspective of spreading dynamics. arXiv preprint arXiv:1505.07354

  26. Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1:215–239

    Article  Google Scholar 

  27. Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41

    Article  Google Scholar 

  28. Bonacich P, Lloyd P (2001) Eigenvector-like measures of centrality for asymmetric relations. Soc Netw 23:191–201

    Article  Google Scholar 

  29. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30:107–117

    Article  Google Scholar 

  30. Lü L, Zhang Y-C, Yeung CH, Zhou T (2011) Leaders in social networks, the delicious case. PLoS ONE 6:e21202

    Article  Google Scholar 

  31. Yu Y, Berger-Wolf TY, Saia J (2010) Finding spread blockers in dynamic networks. In: Advances in social network mining and analysis. Springer, pp 55–76

  32. Ma Q, Ma J (2017) Identifying and ranking influential spreaders in complex networks with consideration of spreading probability. Phys A 465:312–330

    Article  Google Scholar 

  33. Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 199–208

  34. Joyce KE, Laurienti PJ, Burdette JH, Hayasaka S (2010) A new measure of centrality for brain networks. PLoS ONE 5:e12200

    Article  Google Scholar 

  35. Liu J, Xiong Q, Shi W, Shi X, Wang K (2016) Evaluating the importance of nodes in complex networks. Phys A 452:209–219

    Article  Google Scholar 

  36. Krebs V. Political books network. Unpublished, retrieved from Mark Newman’s website: www-personal.umich.edu/~ mejn/netdata/

  37. Guimera R, Danon L, Diaz-Guilera A, Giralt F, Arenas A (2003) Self-similar community structure in a network of human interactions. Phys Rev E 68:065103

    Article  Google Scholar 

  38. Kunegis J (2014) Hamsterster full network dataset—KONECT. http://konect.uni-koblenz.de/networks/petster-hamster. Accessed 01 Mar 2014

  39. Xie N (2006) Social network analysis of blogs. Ph.D. thesis, M.Sc. Dissertation, University of Bristol

  40. Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: densification and shrinking diameters. ACM Trans Knowl Discov Data (TKDD) 1:2

    Article  Google Scholar 

  41. Boguñá M, Pastor-Satorras R, Díaz-Guilera A, Arenas A (2004) Models of social networks based on social distance attachment. Phys Rev E 70:056122

    Article  Google Scholar 

  42. Newman ME (2001) The structure of scientific collaboration networks. Proc Natl Acad Sci 98:404–409

    Article  MathSciNet  Google Scholar 

  43. Castellano C, Pastor-Satorras R (2010) Thresholds for epidemic spreading in networks. Phys Rev Lett 105:218701

    Article  Google Scholar 

  44. Hu H-B, Wang X-F (2008) Unified index to quantifying heterogeneity of complex networks. Phys A 387:3769–3780

    Article  Google Scholar 

  45. Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78:046110

    Article  Google Scholar 

  46. Gupta N, Singh A, Cherifi H (2016) Centrality measures for networks with community structure. Phys A 452:46–59

    Article  Google Scholar 

  47. Taghavian F, Salehi M, Teimouri M (2017) A local immunization strategy for networks with overlapping community structure. Phys A 467:148–156

    Article  Google Scholar 

  48. Wang R-S, Albert R (2013) Effects of community structure on the dynamics of random threshold networks. Phys Rev E 87:012810

    Article  Google Scholar 

  49. Daley D, Gani J (1999) Epidemic modeling: an introduction, vol 228. Cambridge University Press, New York

    Book  Google Scholar 

  50. Kendall MG (1938) A new measure of rank correlation. Biometrika 30:81–93

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology and Communications, Azarbaijan Shahid Madani University, Tabriz, Iran

    Kamal Berahmand, Asgarali Bouyer & Negin Samadi

Authors
  1. Kamal Berahmand
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Asgarali Bouyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Negin Samadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Asgarali Bouyer.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Berahmand, K., Bouyer, A. & Samadi, N. A new local and multidimensional ranking measure to detect spreaders in social networks. Computing 101, 1711–1733 (2019). https://doi.org/10.1007/s00607-018-0684-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-018-0684-8

Keywords

Mathematics Subject Classification

Search

Navigation