Literacy: Relationships and Relations

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


The last chapter has shown that there are different problems concerning the data itself and the definition of the set of entities represented in the network. In this chapter various fallacies with respect to the relations represented in a network are discussed.


Network Analysis Complex Network Cluster Coefficient Network Representation Betweenness 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.
    Barabási A-L (2005) The origin of bursts and heavy tails in human dynamics. Nature 435:207–211CrossRefGoogle Scholar
  2. 2.
    Barrat A, Barthélemy M, Pastor-Satorras R, Vespignani A (2004) The architecture of complex weighted networks. PNAS 101(11):3747–3753CrossRefGoogle Scholar
  3. 3.
    Bearman P, Parigi P (2004) Cloning headless frogs and other important matters: conversation topics and network structure. Soc Forces 83(2):535–557CrossRefGoogle Scholar
  4. 4.
    Bockholt M, Zweig KA (2015) Why is this so hard? Insights from the state space of a simple board game. In: Proceedings of the 1st joint international conference on serious games, pp 147–157Google Scholar
  5. 5.
    boyd D, Crawford K (2011) Six provocations for big data. In: A decade in internet time: symposium on the dynamics of the internet and society, September 2011Google Scholar
  6. 6.
    Brandes U, Robins G, McCranie A, Wasserman S (2013) What is network science? Netw Sci 1(1):EditorialGoogle Scholar
  7. 7.
    Burt RS (1997) A note on social capital and network content. Soc Netw 19:355–373CrossRefGoogle Scholar
  8. 8.
    Butts CT (2009) Revisiting the foundations of network analysis. Science 325(5939):414–416MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    De Choudhury M, Mason WA, Hofman JM, Watts DJ (2010) Inferring relevant social networks from interpersonal communication. In: Proceedings of the World Wide Web conference 2010Google Scholar
  10. 10.
    Colizza V, Barrat A, Barthélemy M, Vespignani A (2005) The role of the airline transportation network in the prediction and predictability of global epidemics. Proc Natl Acad Sci 103(7):2015–2020CrossRefGoogle Scholar
  11. 11.
    Conlan AJK, Eames KTD, Gage JA, von Kirchbach JC, Ross JV, Saenz RA, Gog JR (2011) Measuring social networks in British primary schools through scientific engagement. Proc R Soc Lond B 278(1711):1467–1475CrossRefGoogle Scholar
  12. 12.
    Dall’Asta L, Barrat A, Barthélemy M (2006) Vulnerability of weighted networks. J Stat Mech: Theory Exp 4:P04006Google 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.
    Fortunato S (2010) Community detection in graphs. Phys Rep 486:75–174MathSciNetCrossRefGoogle Scholar
  15. 15.
    Friedkin NE (1983) Horizons of observability and limits of informal control in organizations. Soc Forces 62:54–77CrossRefGoogle Scholar
  16. 16.
    Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99:7821–7826Google Scholar
  17. 17.
    Grannis R (2010) Six degrees of “who cares?”. Amer J Soc 115(4):991–1017CrossRefGoogle Scholar
  18. 18.
    Granovetter MS (1973) The strength of weak ties. Amer J Soc 78(6):1360–1380CrossRefGoogle Scholar
  19. 19.
    Han J-DJ, Dupuy D, Bertin N, Cusick ME, Vidal M (2005) Effect of sampling on topology predictions of protein-protein interaction networks. Nat Bioetchnol 23(7):839–844CrossRefGoogle Scholar
  20. 20.
    Holme P, Saramäki J (2011) Temporal networks. Phys Rep 519(3):97–125CrossRefGoogle Scholar
  21. 21.
    Horvát E-Á, Hanselmann M, Hamprecht FA, Zweig KA (2012) One plus one makes three (for social networks). PLoS ONE 7(4):e34740Google Scholar
  22. 22.
    Horvát E-Á, Zhang JD, Uhlmann S, Sahin Ö, Zweig KA (2013) A network-based method to assess the statistical significance of mild co-regulation effects. PLOS ONE 8(9):e73413Google Scholar
  23. 23.
    Huberman BA, Romero DM, Wu F (2008) Social networks that matter: Twitter under the microscope. First Monday [Online]., December 2008
  24. 24.
    Jeong H, Tombor B, Albert R, Oltvai ZN, BarabÃąsi A-L (2000) The large-scale organization of metabolic networks. Nature 400:107Google Scholar
  25. 25.
    Karsai M, Kivela M, Pan RK, Kaski K, Kertész J, Barabási A-L, Saramäki J (2011) Small but slow world: how network topology and burstiness slow down spreading. Phys Rev E 83:articleID: 025102(R)Google Scholar
  26. 26.
    Kossinets G, Watts DJ (2006) Empirical analysis of an evolving social network. Science 311:88–90MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Krishnamurty B, Willinger W, Gill P, Arlitt M (2011) A Socratic method for validation of measurement-based network research. Comput Commun 34(1):43–53CrossRefGoogle Scholar
  28. 28.
    Marsden PV (1990) Network data and measurement. Annu Rev Soc 16:435–463CrossRefGoogle Scholar
  29. 29.
    Milo R, Itzkovitz S, Kashtan N, Levitt R, Alon U (2004) Response to comment on “Network motifs: simple building blocks of complex networks” and “Superfamilies of evolved and designed networks”. Science 305:1107dCrossRefGoogle Scholar
  30. 30.
    Milo R, Itzkovitz S, Kashtan N, Levitt R, Shen-Orr S, Ayzenshtat I, Sheffer M, Alon U (2004) Superfamilies of evolved and designed networks. Science 303:1538–1542CrossRefGoogle Scholar
  31. 31.
    Morris M (1993) Telling tails explain the discrepancy in sexual partner reports. Nature 365:437–440CrossRefGoogle Scholar
  32. 32.
    Newman MEJ (2004) Analysis of weighted networks. Phys Rev E 70(5):056131Google Scholar
  33. 33.
    Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113CrossRefGoogle Scholar
  34. 34.
    Padgett JF, Ansell CK (1993) Robust action and the rise of the medici, 1400–1434. Amer J Sociol 98(6):1259–1319CrossRefGoogle Scholar
  35. 35.
    Pastor-Satorras R, Vespignani A (2001) Epidemic spreading in scale-free networks. Phys Rev Lett 86(4):3200–3203CrossRefGoogle Scholar
  36. 36.
    Pfitzner R, Scholtes I, Garas A, Tessone CJ, Schweitzer F (2013) Betweenness preference: quantifying correlations in the topological dynamics of temporal networks. Phys Rev Lett 110(19):198701Google Scholar
  37. 37.
    Quintane E, Kleinbaum AM (2011) Matter over mind? e-mail data and the measurement of social networks. Connections 31:22–46Google Scholar
  38. 38.
    Rocha LEC, Liljeros F, Holme P (2011) Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts. PLOS Comput Biol 7(3):e1001109Google Scholar
  39. 39.
    Russell Bernard H, Killworth PD, Sailer L (1981) Summary of research on informant accuracy in network data. Connections 4(3):11–25Google Scholar
  40. 40.
    Russell Bernard H, Shelley GA, Killworth P (1987) How much of a network does the GSS and RSW dredge up? Soc Netw 9:49–61Google Scholar
  41. 41.
    Uzzi B, Spiro J (2005) Collaboration and creativity: the small world problem. AJS 111(2):447–504Google Scholar
  42. 42.
    Viswanath B, Mislove A, Cha M, Gummadi KP (2009) On the evolution of user interaction in facebook. In: Proceedings of the 2nd ACM workshop on online social networks (WOSN’09)Google Scholar
  43. 43.
    Willinger W, Alderson D, Doyle JC (2009) Mathematics and the internet: a source of enormous confusion and great potential. Not AMS 56(5):586–599Google Scholar
  44. 44.
    Wilson C, Boe B, Sala A, Puttaswamy KPN, Zhao BY (2009) User interactions in social networks and their implications. In: Proceedings of the 4th ACM European conference on computer systems, pp 205–218Google Scholar
  45. 45.
    Zweig KA (2011) Good versus optimal: why network analytic methods need more systematic evaluation. Open Comput Sci 1:137–153CrossRefGoogle Scholar
  46. 46.
    Zweig KA (2016) Towards a theoretical framework for analyzing complex linguistic networks. Are word-adjacency networks networks? Springer, Heidelberg, pp 153–163Google 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