Literacy Interpretation

  • Katharina A. ZweigEmail author
Part of the Lecture Notes in Social Networks book series (LNSN)


In this chapter, I mainly discuss the hypothesis-driven approach of network analysis in which a hypothesis about the function and behavior of a complex system is tested by analyzing the structure of a complex network derived from the complex system of interest. This requires a meaningful, context-related interpretation of the values of structural measures applied to the network representation. This interpretation needs to be carefully matched to the question of interest and depends on the chosen method, the chosen network representation, and also the quality of the input data, as discussed in this chapter. The bottom-line of the chapter is that there is no fixed interpretation attached to a certain network analytic measure but that the context always needs to be regarded as well.


Degree Distribution Network Representation Betweenness Centrality Aggregation Strategy Closeness Centrality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Albert R, Jeong H, Barabási A-L (2000) Error and attack tolerance of complex networks. Nature 406:378–382CrossRefGoogle Scholar
  2. 2.
    Alderson DL, Doyle JC (2010) Contrasting views of complexity and their implications for network-centric infrastructures. IEEE Trans Syst Man Cybern-Part A: Syst Hum 40(4):839–852CrossRefGoogle Scholar
  3. 3.
    Arbesman S, Strogatz SH, Vitevitch MS (2010) The structure of phonological networks across multiple languages. Int J Bifurcat Chaos 20:679–685Google Scholar
  4. 4.
    Arita M (2004) The metabolic world of escherichia coli is not small. Proc Natl Acad Sci 101(6):1543–1547Google Scholar
  5. 5.
    Bauer B, Jordán F, Podani J (2009) Node centrality in food webs: rank orders versus distributions. Ecol Complex 7(4):471–477CrossRefGoogle Scholar
  6. 6.
    Bearman P, Parigi P (2004) Cloning headless frogs and other important matters: conversation topics and network structure. Soc Forces 83(2):535–557CrossRefGoogle Scholar
  7. 7.
    Bennett CM, Baird AA, Miller MB, Wolford GL (2009) Neural correlates of interspecies perspective taking in the post-mortem atlantic salmon: an argument for proper multiple comparisons correction. J Serendipitous Unexpected Results 1(1):1–5Google Scholar
  8. 8.
    Bhadra D, Texter P (2004) Airline networks: an econometric framework to analyze domestic U.S. air travel. J Transp Stat 7(1):Paper 6Google Scholar
  9. 9.
    Borgatti SP (2005) Centrality and network flow. Soc Netw 27:55–71CrossRefGoogle Scholar
  10. 10.
    Borgatti SP, Everett MG (2006) A graph-theoretic perspective on centrality. Soc Netw 28:466–484CrossRefGoogle Scholar
  11. 11.
    Brandes U, Erlebach T (eds) (2005) Network analysis—methodological foundations. LNCS, vol 3418. SpringerGoogle Scholar
  12. 12.
    Butts CT (2009) Revisiting the foundations of network analysis. Science 325(5939):414–416MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Dorn I, Lindenblatt A, Zweig KA (2012) The trilemma of network analysis. In: Proceedings of the 2012 IEEE/ACM international conference on advances in social network analysis and mining, IstanbulGoogle Scholar
  14. 14.
    Doyle JC, Alderson DL, Li L, Low S, Roughan M, Shalunov S, Tanaka R, Willinger W (2005) The “robust yet fragile” nature of the Internet. Proc Natl Acad Sci 102(41):14497–14502CrossRefGoogle Scholar
  15. 15.
    Everett M, Borgatti SP (2005) Ego network betweenness. Soc Netw 27:31–38CrossRefGoogle Scholar
  16. 16.
    Freeman LC (1979) Centrality in networks: I. Conceptual clarifications. Soc Netw 1:215–239CrossRefGoogle Scholar
  17. 17.
    Linton Clarke Freeman (1977) A set of measures of centrality based upon betweenness. Sociometry 40:35–41CrossRefGoogle Scholar
  18. 18.
    Guimerá R, Amaral LAN (2004) Modeling the world-wide airport network. Eur Phys J B 38:381–385Google Scholar
  19. 19.
    Guimerá R, Mossa S, Turtschi A, Amaral LAN (2005) The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles. Proc Natl Acad Sci 102:7794–7799Google Scholar
  20. 20.
    Handcock MS, Jones JH, Morris M (2003) On sexual contacts and epidemic thresholds, models and inference for sexual partnership distributions. Technical report working paper #31, Center for Statistics and the Social Sciences, University of WashingtonGoogle Scholar
  21. 21.
    Höfer T, Przyrembel H, Verleger S (2004) New evidence for the theory of the stork. Pediatr Perinat Epidemiol 18(1):88–92CrossRefGoogle Scholar
  22. 22.
    Holme P, Kim BJ, Yoon CN, Han SK (2002) Attack vulnerability of complex networks. Phys Rev E 65:056109Google Scholar
  23. 23.
    Jacob R, Koschützki D, Lehmann KA, Peeters L, Tenfelde-Podehl D (2005) Network analysis—methodological foundations. Algorithms for centrality indices. SpringerGoogle Scholar
  24. 24.
    Jeong H, Tombor B, Albert R, Oltvai ZN, BarabÃa̧si A-L (2000) The large-scale organization of metabolic networks. Nature 400:107Google Scholar
  25. 25.
    Jones JH, Handcock MS (2003) An assessment of preferential attachment as a mechanism for human sexual network formation. Proc R Soc Lond B 270:1123–1128Google Scholar
  26. 26.
    Jones JH, Handcock MS (2003) Sexual contacts and epidemic thresholds. Nature 423:605–606Google Scholar
  27. 27.
    Jordán F, Benedek Z, Podani J (2007) Quantifying positional importance in food webs: a comparison of centrality indices. Ecol Model 205(1–2):270–275CrossRefGoogle Scholar
  28. 28.
    Kleinberg J (2000) Navigation in a small world. Nature 406:845CrossRefGoogle Scholar
  29. 29.
    Kleinberg J (2000) The small-world phenomenon: an algorithmic perspective. In: Proceedings of the 32nd ACM symposium on theory of computing, pp 163–170Google Scholar
  30. 30.
    Koschützki D, Lehmann KA, Peeters L, Richter S, Tenfelde-Podehl D, Zlotowski O (2005) Network analysis—methodological foundations. Centrality indices. LNCS, vol 3418 of Brandes and Erlebach [12], pp 16–60Google Scholar
  31. 31.
    Koschützki D, Lehmann KA, Tenfelde-Podehl D, Zlotowski O (2005) Network analysis—methodological foundations. Advanced centrality concepts. LNCS, vol 3418 of Brandes and Erlebach [12], pp 83–110Google Scholar
  32. 32.
    Liljeros F, Edling CR, Nunes Amaral LA, Eugene Stanley H, Åberg Y (2001) The web of human sexual contacts. Nature 411:907–908Google Scholar
  33. 33.
    Liljeros F, Edling CR, Nunes Amaral LA (2003) Sexual networks:implications for the transmission of sexually transmitted infections. MicrobesInfect 5:189–196Google Scholar
  34. 34.
    Ma H, Zeng A-P (2003) Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics 19(2):270–277CrossRefGoogle Scholar
  35. 35.
    Newman MEJ (2002) Assortative mixing in networks. Phys Rev Lett 89:208701Google Scholar
  36. 36.
    Newman MEJ (2005) A measure of betweenness centrality based on random walks. Soc Netw 27:39–54Google Scholar
  37. 37.
    Newman MEJ, Forrest S, Balthrop J (2002) Email networks and the spread of computer viruses. Phys Rev E 66:035101(R)Google Scholar
  38. 38.
    Pastor-Satorras R, Vespignani A (2001) Epidemic spreading in scale-free networks. Phys Rev Lett 86(4):3200–3203CrossRefGoogle Scholar
  39. 39.
    Pitts FR (1965) A graph theoretic approach to historical geography. Prof Geogr 17(5):15–20CrossRefGoogle Scholar
  40. 40.
    Pitts FR (1978/79) The medieval river trade network of Russia revisited. Soc Netw 1:285–292Google Scholar
  41. 41.
    Quintane E, Kleinbaum AM (2011) Matter over mind? e-mail data and the measurement of social networks. Connections 31:22–46Google Scholar
  42. 42.
    Russell Bernard H, Killworth PD, Sailer L (1981) Summary of research on informant accuracy in network data. Connections, 4(3):11–25Google Scholar
  43. 43.
    Silver N (2012) The signal and the noise: why most predictions fail but some don’t. Penguin Books Ltd., LondonGoogle Scholar
  44. 44.
    Sudarshan Iyengar SR, Zweig K, Natarajan A, Veni Madhavan CE (2011) A network analysis approach to understand human-wayfinding problem. In: Proceedings of the 33rd annual meeting of the cognitive science societyGoogle Scholar
  45. 45.
    Sudarshan Iyengar SR, Veni Madhavan CE, Zweig KA, Natarajan A (2012) Understanding human navigation using network analysis. TopiCS—Topics Cognitive Sci 4(1):121–134Google Scholar
  46. 46.
    Spitz A, Gimmler A, Stoeck T, Zweig KA, Horvát E-Á (2016) Assessing low intensity relationships in complex networks. PLoS ONE 11(4):e0152536Google Scholar
  47. 47.
    Tavassoli S, Zweig KA (2016) Most central or least central? How much modeling decisions influence a node’s centrality ranking in multiplex networks. ArXiv e-prints,
  48. 48.
    Watts DJ (1999) Small worlds—the dynamics of networks between order and randomness. Princeton studies in complexity. Princeton University PressGoogle Scholar
  49. 49.
    Zhou S, Mondragon RJ (2004) The rich-club phenomenon in the internet topology. IEEE Commun Lett 8:180–182CrossRefGoogle Scholar
  50. 50.
    Zweig KA (2011) Good versus optimal: why network analytic methods need more systematic evaluation. Open Comput Sci 1:137–153CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria 2016

Authors and Affiliations

  1. 1.TU Kaiserslautern, FB Computer ScienceGraph Theory and Analysis of Complex NetworksKaiserslauternGermany

Personalised recommendations