Skip to main content
SpringerLink
Log in
Book cover

Data-Centric Business and Applications pp 67–90Cite as

Influence and Betweenness in Flow Models of Complex Network Systems

  • Chapter
  • First Online:

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 48))

Abstract

This paper provides the analysis for functional approaches of complex network systems research. In order to study the behavior of these systems the flow adjacency matrices were introduced. The concepts of strength, power, domain and diameter of influence of complex network nodes are analyzed for the purpose of determining their importance in the systems structure. The notions of measure, power, domain and diameter of betweenness of network nodes and edges are introduced to identify their significance in the operation process of network systems. These indicators quantitatively express the contribution of the corresponding element for the motion of flows in the system and determine the losses that are expected in the case of blocking this node or edge or targeted attack on it. Similar notions of influence and betweenness are introduced to determine the functional importance of separate subsystems of network system and the system as a whole. Examples of practical use of the obtained results in information processing and management are given.

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. Boccara N (2010) Modeling complex systems. Springer Science & Business Media, New York

    Book  Google Scholar 

  2. Barabási A-L, Frangos J (2002) Linked: the new science of networks. Basic Books, New York

    Google Scholar 

  3. Boccaletti S et al (2006) Complex networks: structure and dynamics. Phys Rep 424(4):175–308. https://doi.org/10.1016/j.physrep.2005.10.009

    Article  MathSciNet  MATH  Google Scholar 

  4. Dorogovtsev SN, Mendes JFF (2013) Evolution of networks: from biological nets to the internet and www. Oxford University Press, Oxford

    MATH  Google Scholar 

  5. Caldarelli G, Vespignani A (2007) Large scale structure and dynamics of complex networks: from information technology to finance and natural science. World Scientific, New York

    Book  Google Scholar 

  6. Northrop RB (2011) Introduction to complexity and complex systems. CRC Press, Boca Raton

    Google Scholar 

  7. Barrat F, Barthélemy M, Vespignani A (2007) The architecture of complex weighted networks: measurements and models. Large scale structure and dynamics of complex networks. World Scientific, London, pp 67–92

    Chapter  Google Scholar 

  8. Newman MEJ (2004) Analysis of weighted networks. Phys Rev E 70:056131. https://doi.org/10.1103/PhysRevE.70.056131

    Article  Google Scholar 

  9. Polishchuk DO, Polishchuk OD, Yadzhak MS (2016) Complex deterministic evaluation of hierarchically-network systems: IV. Interactive evaluation. Syst Res Inf Technol 1:7–16. https://doi.org/10.20535/SRIT.2308-8893.2016.1.01

    Article  Google Scholar 

  10. Polishchuk O (2001) Optimization of evaluation of man’s musculo-sceletal system. Comput Math 2:360–367

    Google Scholar 

  11. Ageyev DV, Salah MT (2016) Parametric synthesis of overlay networks with self-similar traffic. Telecommun Radio Eng 75(14):1231–1241 (English translation of Elektrosvyaz and Radiotekhnika)

    Article  Google Scholar 

  12. Ageyev D et al (2019) Infocommunication networks design with self-similar traffic. In: 2019 IEEE 15th international conference on the experience of designing and application of CAD systems (CADSM). IEEE, pp 24–27. https://doi.org/10.1109/cadsm.2019.8779314

  13. Daradkeh YI, Kirichenko L, Radivilova T (2018) Development of QoS methods in the information networks with fractal traffic. Int J Electron Telecommun 64(1):27–32

    Google Scholar 

  14. Albert R, Barabasi A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47. https://doi.org/10.1103/RevModPhys.74.47

    Article  MathSciNet  MATH  Google Scholar 

  15. Newman MEJ (2010) Networks. An introduction. Oxford University Press, Oxford

    Book  Google Scholar 

  16. Polishchuk O, Yadzhak M (2018) Network structures and systems: I. flow characteristics of complex networks. Syst Res Inf Technol 2:42–54. https://doi.org/10.20535/SRIT.2308-8893.2018.2.05

    Article  Google Scholar 

  17. Lombardi A, Hörnquist M (2007) Controllability analysis of networks. Phys Rev E 75(5):056110. https://doi.org/10.1103/PhysRevE.75.056110

    Article  Google Scholar 

  18. Liu Y-Y, Slotine JJ, Barabási A-L (2013) Observability of complex systems. Proc Natl Acad Sci 110(7):2460–2465. https://doi.org/10.1073/pnas.1215508110

    Article  MathSciNet  MATH  Google Scholar 

  19. Polishchuk D, Polishchuk O, Yadzhak M (2014) Complex evaluation of hierarchically-network systems. Autom Control Inf Sci 1(2):32–44. https://doi.org/10.12691/acis-2-2-1

    Article  Google Scholar 

  20. Polishchuk O (2018) Flow models of complex network systems. In: Intern. Scientific-practical conf. on problems of infocommunications, science and technology, pp 317–322

    Google Scholar 

  21. Polishchuk OD, Tyutyunnyk MI, Yadzhak MS (2007) Quality evaluation of complex systems function on the base of parallel calculations. Inf Extr Process 26(102):121–126

    Google Scholar 

  22. Zurek WH (2018) Complexity, entropy and the physics of information. CRC Press, Boca Raton

    Book  Google Scholar 

  23. Kryvinska N (2004) Intelligent network analysis by closed queuing models. Telecommun Syst 27:85–98. https://doi.org/10.1023/B:TELS.0000032945.92937.8f

    Article  Google Scholar 

  24. Prell A (2012) Social network analysis: history, theory and methodology. SAGE, New York

    Google Scholar 

  25. Price G, Sherman C (2001) The invisible web: uncovering information sources search engines can’t see. CyberAge Books, New York

    Google Scholar 

  26. Bonacich P (1987) Power and centrality: a family of measures. Am J Sociol 92(5):1170–1182. https://doi.org/10.1086/228631

    Article  Google Scholar 

  27. Glenn L (2015) Understanding the influence of all nodes in a network. Sci Rep 5:8665. https://doi.org/10.1038/srep08665

    Article  Google Scholar 

  28. Cao Q et al (2012) Aiding the detection of fake accounts in large scale social online services. In: 9th USENIX symposium on networked systems design and implementation, pp 197–210

    Google Scholar 

  29. Abokhodair N, Yoo D, McDonald DW (2015) Dissecting a social botnet: growth, content and influence in Twitter. In: 18th ACM conference on computer supported cooperative work & social computing, pp 839–851

    Google Scholar 

  30. Buldyrev SV et al (2010) Catastrophic cascade of failures in interdependent networks. Nature 464:1025–1028. https://doi.org/10.1038/nature08932

    Article  Google Scholar 

  31. Brockwell PJ, Davis RA (2002) Introduction to time series and forecasting. Springer, Switzerland

    Book  Google Scholar 

  32. Juher D, Ripoll J, Saldaña J (2013) Outbreak analysis of an SIS epidemic model with rewiring. J Math Biol 67(2):411–432. https://doi.org/10.1007/s00285-012-0555-4

    Article  MathSciNet  MATH  Google Scholar 

  33. Albert R, Jeong H, Barabási A-L (2000) Error and attack tolerance of complex networks. Nature 406:378–482. https://doi.org/10.1038/35019019

    Article  Google Scholar 

  34. Holme P et al (2002) Attack vulnerability of complex networks. Phys Rev E 65:056109. https://doi.org/10.1103/PhysRevE.65.056109

    Article  Google Scholar 

  35. Polishchuk O, Polishchuk D (2013) Monitoring of flow in transport networks with partially ordered motion. In: XXIII conf. Carpenko physics and mechanics institute, NASU, Lviv, pp 326–329

    Google Scholar 

  36. Borgatti SP (2005) Centrality and network flow. Soc Netw 27(1):55–71. https://doi.org/10.1016/j.socnet.2004.11.008

    Article  MathSciNet  Google Scholar 

  37. Freeman LC (1979) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239. https://doi.org/10.1016/0378-8733(79)90002-9

    Article  MathSciNet  Google Scholar 

  38. Bavelas A (1950) Communication patterns in task-oriented groups. J Am Acoust Soc 22(6):725–730. https://doi.org/10.1121/1.1906679

    Article  Google Scholar 

  39. Freeman LC (1977) A set of measures of centrality based upon betweenness. Sociometry 40:35–41. https://doi.org/10.2307/3033543

    Article  Google Scholar 

  40. Bonacich P, Lloyd P (2001) Eigenvector-like measures of centrality for asymmetric relations. Soc Netw 23(3):191–201. https://doi.org/10.1016/S0378-8733(01)00038-7

    Article  Google Scholar 

  41. Piraveenan M (2013) Percolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks. PLoS ONE 8(1):e53095. https://doi.org/10.1371/journal.pone.0053095

    Article  Google Scholar 

  42. Faghani M, Nguyen UT (2013) A study of XSS worm propagation and detection mechanisms in online social networks. IEEE Trans Inf Forensics Secur 8(11):1815–1826. https://doi.org/10.1109/TIFS.2013.2280884

    Article  Google Scholar 

  43. Katz L (1953) A new status index derived from sociometric index. Psychometrika 18(1):39–43. https://doi.org/10.1007/BF02289026

    Article  MATH  Google Scholar 

  44. Marchiori M, Latora V (2000) Harmony in the small-world. Phys A: Stat Mech Its Appl 285(3–4):539–546. https://doi.org/10.1016/S0378-4371(00)00311-3

    Article  MATH  Google Scholar 

  45. Krackhardt D (1990) Assessing the political landscape: structure, cognition, and power in organizations. Adm Sci Q 35(2):342–369. https://doi.org/10.2307/2393394

    Article  Google Scholar 

  46. Polishchuk O, Yadzhak M (2018) Network structures and systems: III. Hierarchies and networks. Syst Res Inf Technol 4:82–95. https://doi.org/10.20535/SRIT.2308-8893.2018.4.07

    Article  Google Scholar 

  47. Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99(12):7821–7826. https://doi.org/10.1073/pnas.122653799

    Article  MathSciNet  MATH  Google Scholar 

  48. Newman MEJ (2004) Detecting community structure in networks. Eur Phys J B 38(2):321–330. https://doi.org/10.1140/epjb/e2004-00124-y

    Article  Google Scholar 

  49. Newman MEJ (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69(6):066133. https://doi.org/10.1103/PhysRevE.69.066133

    Article  Google Scholar 

  50. Danon L et al (2005) Comparing community structure identification. J Stat Mech 09:P09008. https://doi.org/10.1088/1742-5468/2005/09/P09008

    Article  MATH  Google Scholar 

  51. Blondel VD et al (2008) Fast unfolding of community hierarchies in large networks. J Stat Mech 10:P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008

    Article  MATH  Google Scholar 

  52. Dorogovtsev SN, Goltsev AV, Mendes JFF (2006) k-core organization of complex networks. Phys Rev Lett 96(4): 040601. https://doi.org/10.1103/physrevlett.96.040601

  53. Polishchuk O, Yadzhak M (2018) Network structures and systems: II. Cores of networks and multiplexes. Syst Res Inf Technol 3:38–51. https://doi.org/10.20535/SRIT.2308-8893.2018.3.04

    Article  Google Scholar 

  54. Scott WR (2015) Organizations and organizing: rational, natural and open systems perspectives. Routledge, London

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Modeling and Optimization of Complex Systems, Ya. S. Pidstryhach Institute for Applied Problems of Mechanics and Mathematics National Academy of Sciences of Ukraine, Lviv, Ukraine

    Olexandr Polishchuk

Authors
  1. Olexandr Polishchuk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Olexandr Polishchuk .

Editor information

Editors and Affiliations

  1. V.V. Popovskyy department of Infocommunication Engineering, Faculty of Infocommunication, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Tamara Radivilova

  2. V.V. Popovskyy department of Infocommunication Engineering, Faculty of Infocommunication, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Dmytro Ageyev

  3. Department of e-Business, Faculty of Business, Economics and Statistics, University of Vienna, Vienna, Austria

    Natalia Kryvinska

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Polishchuk, O. (2021). Influence and Betweenness in Flow Models of Complex Network Systems. In: Radivilova, T., Ageyev, D., Kryvinska, N. (eds) Data-Centric Business and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-030-43070-2_5

Download citation

Publish with us

Policies and ethics

Search

Navigation