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A new method for identifying influential nodes and important edges in complex networks

  • Computer Science
  • Published:
Wuhan University Journal of Natural Sciences

Abstract

The identification of the influential nodes in a network is of great significance for understanding the features of the network and controlling the complexity of networks in society and in biology. In this paper, we propose a novel centrality measure for a node by considering the importance of edges and compare the performance of this method with existing seven topological-based ranking methods on the Susceptible-Infected-Recovered (SIR) model. The simulation results for four different types of real networks show that the proposed method is robust and exhibits excellent performance in identifying the most influential nodes when spreading starting from both single origin and multipleorigins simultaneously.

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References

  1. Pastor-Satorras R, Vespignani A. Epidemic spreading in scale-free networks [J]. Phys Rev Lett, 2001, 86: 3200–3203.

    Article  CAS  PubMed  Google Scholar 

  2. Borge Holthoefer J, Moreno Y. Absence of influential spreaders in rumor dynamics [J]. Phys Rev E, 2012, 85: 026116.

    Article  Google Scholar 

  3. Marcelino J, Kaiser M. Critical paths in a metapopulation model of H1N1: Efficiency delaying influenza spreading through flight cancellation [J]. PLOS Curr, 2012, 4(19): p.e4f8c9a2e1fca8.

  4. Jin S, Li Y, Pan R, et al. Characterizing and controlling the inflammatory network during influenza a virus infection [J]. Scientific Reports, 2014, 4: 3799.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Klemm K, Serrano M, Eguiluz V, et al. A measure of individual role in collective dynamics: spreading at criticality [J]. Scientific Reports, 2012, 2: 292.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Tan J, Zou X. Complex dynamical analysis of a coupled system from innate immune responses[J]. International Journal of Bifurcation and Chaos, 2013, 23(11): 1350180.

    Article  Google Scholar 

  7. Hébert-Dufresne L, Allard A, Young J G, et al. Global efficiency of local immunization on complex networks [J]. Scientific Reports, 2013, 3(2171): 1–8.

    Google Scholar 

  8. Wuellner D R, Roy S, Souza R M D. Resilience and rewiring of the passenger airline networks in the United States [J]. Phys Rev E, 2010, 82: 056101.

    Article  Google Scholar 

  9. Albert R, Jeong H, Barabási A L. Error and attack tolerance of complex networks [J]. Nature, 2000, 406: 378–382.

    Article  CAS  PubMed  Google Scholar 

  10. Tan J, Zou X, Optimal control strategy for abnormal innate immune response [J]. Computational and Mathematical Methods in Medicine, 2015, 2015: 386235.

    PubMed  PubMed Central  Google Scholar 

  11. Holman A G, Davis P J, Foster J M, et al. Computational prediction of essential genes in an unculturable endosymbiotic bacterium, Wolbachia of Brugiamalayi [J]. BMC Microbiology, 2009, 9:243–257.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Lamichhane G, Zignol M, Blades N J, et al. A postgenomic method for predicting essential genes at subsaturation levels of mutagenesis: application to mycobacterium tuberculosis [J]. PNAS, 2003, 100(12): 7213–7218.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Li Y, Jin S, Lei L, et al. Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis [J]. Scientific Reports, 2015, 5:9283.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Freeman L C. A set of measures of centrality based upon betweenness [J]. Sociometry, 1977, 40: 35–41.

    Article  Google Scholar 

  15. Jeong H, Mason S P, Barabási A L, et al. Lethality and centrality in protein networks [J]. Nature, 2001, 411:41–42.

    Article  CAS  PubMed  Google Scholar 

  16. Liu Y Y, Slotine J J, Barabási A L. Controllability of complex networks [J]. Nature, 2011, 473(7346): 167–173.

    Article  CAS  PubMed  Google Scholar 

  17. Kitsak M, Gallos L K, Havlin S, et al. Identifying influential spreaders in complex networks [J]. Nature Physics, 2010, 6:888–893.

    Article  CAS  Google Scholar 

  18. Zeng A, Zhang C J. Ranking spreaders by decomposing complex networks [J]. Physics Letters A, 2013, 337(14): 1031–1035.

    Article  Google Scholar 

  19. Chen D B, Lü LY, Shang M S, et al. Identifying influential nodes in complex networks [J]. Physica A, 2012, 391: 1777–1787.

    Article  Google Scholar 

  20. Gao S, Ma J, Chen Z M, et al. Ranking the spreading ability of nodes in complex networks based on local structure [J]. Physica A, 2014, 403:130–147.

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Huberman B A, Adamic L A. Growth dynamics of the-World-Wide Web [J]. Nature, 1999, 401:131–132.

    CAS  Google Scholar 

  23. Faloutsos M, Faloutsos P, Faloutsos C. On power-law relationships of the internet topology [J]. Comput Commun Rev, 1999, 29:251–262.

    Article  Google Scholar 

  24. Khanin R, Wit E. How scale-free are biological networks [J]. J Comput Biol, 2006, 13(3): 810–818.

    Article  CAS  PubMed  Google Scholar 

  25. Noort V V, Snel B, Huynen M A. The yeast coexpression network has a small-world, scale-free architecture and can be explained by a simple model [J]. EMBO Reports, 2004, 5: 280–284.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Jeong H, Tombor B, Albert R, et al. The large scale organization of metabolic networks [J]. Nature, 2000, 407:651–654.

    Article  CAS  PubMed  Google Scholar 

  27. Albert R, Barabási A L. Statistical mechanics of complex networks [J]. Reviews of Modern Physics, 2002, 74:47–97.

    Article  Google Scholar 

  28. Xie N. Social Network Analysis of Blogs [D]. Bristol: University of Bristol, 2006.

    Google Scholar 

  29. Newman M E J. Finding community structure in networks using the eigenvectors of matrics [J]. Phys Rev E, 2006, 74: 036104.

    Article  CAS  Google Scholar 

  30. Spring N, Mahajan R, Wetherall D, et al. Measuring ISP topologies with rocketfuel [J]. IEEE/ACM Trans Netw, 2004, 12:2–16.

    Article  Google Scholar 

  31. Watts D J, Strogatz S H. Collective dynamics of “small-world” networks [J]. Nature, 1998, 393: 440–442.

    Article  CAS  PubMed  Google Scholar 

  32. Chung F, Lu L, Dewey T G, et al. Duplication models for biological Networks [J]. J Comput Biol, 2003, 10(5): 677–687.

    Article  CAS  PubMed  Google Scholar 

  33. Clauset A, Shalizi C R, Newman M E J. Power-law distributions in empirical data [J]. SIAM Review, 2009, 51: 661–703.

    Article  Google Scholar 

  34. Dickmann O, Heesterbeek J A P. Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation [M]. New York: Wiley Press, 2000:297–298.

    Google Scholar 

  35. Zhang W, Zou X. A new method for detecting protein complexes based on the three node cliques [J]. IEEE/ACM Trans Comput Biol Bioinform, 2014, 99.1, doi: 10.1109/TCBB.2014.2386314.

    Google Scholar 

  36. Capocci A, Servedio V D P, Caldarelli G, et al. Detecting communities in large networks [J]. Physica A, 2005, 352: 669–676.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Science, East China Jiaotong University, Nanchang 330013, Jiangxi, China

    Wei Zhang

  2. School of Mechatronic Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China

    Jia Xu

  3. School of Science, Wuhan Institute of Technology, Wuhan 430074, Hubei, China

    Yuanyuan Li

Authors
  1. Wei Zhang
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  2. Jia Xu
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  3. Yuanyuan Li
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Corresponding author

Correspondence to Wei Zhang.

Additional information

Foundation item: Supported by the Research Foundation of Hubei Province Department of Education (Q20151505) and the East China Jiaotong University Doctor Scientific Research Start Fund Project (26441021)

Biography: ZHANG Wei, male, Ph.D., research direction: computational system biology, complex network.

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Cite this article

Zhang, W., Xu, J. & Li, Y. A new method for identifying influential nodes and important edges in complex networks. Wuhan Univ. J. Nat. Sci. 21, 267–276 (2016). https://doi.org/10.1007/s11859-016-1170-9

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  • DOI: https://doi.org/10.1007/s11859-016-1170-9

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