Skip to main content
SpringerLink
Log in
Book cover

Coordination Control of Distributed Systems pp 265–272Cite as

Detecting Influential Nodes in Complex Networks with Range Probabilistic Control Centrality

  • Chapter
  • First Online:

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 456))

Abstract

Dynamic complex networks illustrate how ‘agents’ interact by exchanging information, in a network that is constantly changing; an example of such networks is a vehicular ad hoc network. This article investigates the issue of influence propagation in dynamic, complex networks, and in particular, it proposes a method for identifying influential nodes in a network with probabilistic links. Based on control-theoretic concepts, we develop the range probabilistic control centrality (RPCC). For evaluation purposes, we used the susceptible, infected, recovered (SIR) model, which is simple model for epidemic spreading assuming no births or deaths, accepting that the incubation period of the infectious agent is instantaneous, and that the duration of infectivity is same as length of the disease; it also assumes a completely homogeneous population with no age, spatial, or social structure. Our experimentation shows that the proposed identification method is able to recognize very effective spreaders. The key feature of these nodes is that they are positioned at the beginning of ‘strong’ paths, upon which paths a large number of other nodes lies.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Newman MEJ, (2010) Networks: aN iNtroductioN. Oxford UNiversity Press, Cambridge

    Google Scholar 

  2. Doerr B, Fouz M, Friedrich T, (2011) Social networks spread rumors in sublogarithmic time, In Proceedings of ACM STOC, 2011, pp 21–30

    Google Scholar 

  3. Langville A, Meyer C (2006) Google’s PageRank and beyond: the science of search engine rankings. Princeton University Press, New Jersey

    Google Scholar 

  4. Basaras P, Katsaros D, Tassiulas L (2013) Detecting influential spreaders in complex, dynamic networks. IEEE Comput Mag 46(4):26–31

    Article  Google Scholar 

  5. 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 

  6. Pallis G, Katsaros D, Dikaiakos MD, Loulloudes N, Tassiulas L (2009) On the structure and evolution of vehicular networks. In: Proceedings of IEEE/ACM MASCOTS, pp 502–511

    Google Scholar 

  7. Fountalis I, Bracco A, Dovrolis C (2013) Spatio-temporal network analysis for studying climate patterns. Climate Dynamics, 2013, ( to appear)

    Google Scholar 

  8. Barrat A, Barthelemy M, Pastor-Satorras R, Vespignani A (2004) The architecture of complex weighted networks. Proc Nat Acad Sci 101(11):3747–3752

    Article  Google Scholar 

  9. Lambiotte R, Blondel VD, de Kerchove C, Huens E, Prieur C, Smoreda Z, Van Dooren P (2008) Geographical dispersal of mobile communication networks. Physica, 1(1)

    Google Scholar 

  10. Li Y, Chen W, Wang Y, Zhang Z.-L (2013) Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships. In Proceedings of ACM WSDM, 2013

    Google Scholar 

  11. Y.-Y. Liu, J.-J. Slotine, and A.-L. Barabasi (2012) Control centrality and hierarchical structure in complex networks. PLOS One, 7(9)

    Google Scholar 

  12. Hosoe S (1980) Determination of generic dimensions of controllable subspaces and its applications. IEEE Trans Autom Control 25(6):1192–1196

    Article  MathSciNet  MATH  Google Scholar 

  13. The Stanford Network Analysis Project, Available at http://snap.stanford.edu/data

  14. Pei S, Muchnik L, Andrade Jr J, Zheng JSZ, Makse HA (2014) Searching for superspreaders of information in real-world social media, 2014, Available at http://arxiv.org/abs/1405.1790

Download references

Author information

Authors and Affiliations

  1. Electrical and Computer Engineering Department, University of Thessaly, Volos, Greece

    Dimitrios Katsaros & Pavlos Basaras

Authors
  1. Dimitrios Katsaros
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pavlos Basaras
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dimitrios Katsaros .

Editor information

Editors and Affiliations

  1. Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands

    Jan H. van Schuppen

  2. Dipartimento d'Informatica, Università degli studi di Verona, Verona, Italy

    Tiziano Villa

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Katsaros, D., Basaras, P. (2015). Detecting Influential Nodes in Complex Networks with Range Probabilistic Control Centrality. In: van Schuppen, J., Villa, T. (eds) Coordination Control of Distributed Systems. Lecture Notes in Control and Information Sciences, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-10407-2_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-10407-2_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10406-5

  • Online ISBN: 978-3-319-10407-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation