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Literacy: Relationships and Relations

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Network Analysis Literacy

Part of the book series: Lecture Notes in Social Networks ((LNSN))

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Abstract

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.

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Notes

  1. 1.

    Newman states that the edge betweenness centrality can be computed by computing the edge betweenness centrality on the simplified graph (i.e., unweighted graph) and then, for each edge, derived by dividing the corresponding value by the weight of the edge. Note that the algorithm described by Newman does not compute the same edge betweenness than if the multi-edges are kept as separate entities. The latter version will in general increase the number of shortest paths between two nodes (s. Exercise 9.10).

  2. 2.

    The weight on an edge was defined as the geometric average \(\sqrt{w_{ij}w_{ji}}\) where \(w_{ij}\) denotes the average number of messages sent per year from person i to person j over some multi-year span.

  3. 3.

    The article of Bearman and Parigi is called: “Cloning Headless Frogs and Other Important Matters: Conversation Topics and Network Structure” for a reason ;-).

  4. 4.

    They note that their results are robust with respect to other choices of a threshold for excluding emails. They argue for their choice of 5 as follows: “Mass mailings typically consist of factual information that must be broadcast to multiple people simultaneously; as such, they are unlikely to contain socially meaningful interpersonal interactions. The choice of a particular threshold is inherently arbitrary; we chose a threshold of four because it eliminates the most obvious mass mailings while preserving over \(93\,\%\) of e-mails in our sample and because it is similar to choices made by other scholars” [37]. They also discuss the second, more implicit threshold, the requirement that one interaction in a three month period is enough to establish a connection between two persons: “In the organization we study, three months appears to offer the optimal balance between stability and fluidity [of interactions]” [37].

  5. 5.

    Of course, also in digital data there are errors. For example, not every sent email is successfully delivered and logs might get lost before they are stored on a permanent server.

  6. 6.

    Note, however, in a combination of qualitative and quantitative analysis, Padgett and Ansell use network analysis on marriages between important Florentine families to understand the position of the Medici family in Florentine [34]. Thus, the individual level is aggregated to the level of families. The authors also embed this analysis into the analyses of other important relationships such as patronage and economic connections.

  7. 7.

    Possibly and most likely, the words will be stemmed, i.e., all derived or inflected words are represented by the associated word stem or root word.

  8. 8.

    Please refrain from sending me sentences in which “political contact lens” makes totally sense, I beg you, dear reader! I am sure one can come up with such a monstrous sentence.

  9. 9.

    However, describing the paths on which this happens will in general not boil down to “all shortest paths between any two vertices”. It is thus most likely that an analysis of such a network still requires new tools and methods than, e.g., classic network centralities.

  10. 10.

    Thanks to Valdis Krebs for pointing me to this paper when I asked him for his choice of the most undercited paper in social network analysis.

  11. 11.

    Numbers are estimated from Fig. 1a in [26].

  12. 12.

    Note that the email contact network is used as a proxy for personal face-to-face communication. Kossinets and Watts argue that in a closed social environment as a university this is quite likely to be true such that not many false-positive or false-negative edges are to be expected.

  13. 13.

    The part in bold is by Krishnamurty et al., the explanation after that clarifies the original statement from my point of view.

References

  1. Barabási A-L (2005) The origin of bursts and heavy tails in human dynamics. Nature 435:207–211

    Article  Google Scholar 

  2. Barrat A, Barthélemy M, Pastor-Satorras R, Vespignani A (2004) The architecture of complex weighted networks. PNAS 101(11):3747–3753

    Article  Google Scholar 

  3. Bearman P, Parigi P (2004) Cloning headless frogs and other important matters: conversation topics and network structure. Soc Forces 83(2):535–557

    Article  Google Scholar 

  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 147–157

    Google Scholar 

  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 2011

    Google Scholar 

  6. Brandes U, Robins G, McCranie A, Wasserman S (2013) What is network science? Netw Sci 1(1):Editorial

    Google Scholar 

  7. Burt RS (1997) A note on social capital and network content. Soc Netw 19:355–373

    Article  Google Scholar 

  8. Butts CT (2009) Revisiting the foundations of network analysis. Science 325(5939):414–416

    Article  MathSciNet  MATH  Google Scholar 

  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 2010

    Google Scholar 

  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):2015–2020

    Article  Google Scholar 

  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):1467–1475

    Article  Google Scholar 

  12. Dall’Asta L, Barrat A, Barthélemy M (2006) Vulnerability of weighted networks. J Stat Mech: Theory Exp 4:P04006

    Google Scholar 

  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, Istanbul

    Google Scholar 

  14. Fortunato S (2010) Community detection in graphs. Phys Rep 486:75–174

    Article  MathSciNet  Google Scholar 

  15. Friedkin NE (1983) Horizons of observability and limits of informal control in organizations. Soc Forces 62:54–77

    Article  Google Scholar 

  16. Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99:7821–7826

    Google Scholar 

  17. Grannis R (2010) Six degrees of “who cares?”. Amer J Soc 115(4):991–1017

    Article  Google Scholar 

  18. Granovetter MS (1973) The strength of weak ties. Amer J Soc 78(6):1360–1380

    Article  Google Scholar 

  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):839–844

    Article  Google Scholar 

  20. Holme P, Saramäki J (2011) Temporal networks. Phys Rep 519(3):97–125

    Article  Google Scholar 

  21. Horvát E-Á, Hanselmann M, Hamprecht FA, Zweig KA (2012) One plus one makes three (for social networks). PLoS ONE 7(4):e34740

    Google Scholar 

  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):e73413

    Google Scholar 

  23. Huberman BA, Romero DM, Wu F (2008) Social networks that matter: Twitter under the microscope. First Monday [Online]. http://firstmonday.org/ojs/index.php/fm/article/view/2317/2063, December 2008

  24. Jeong H, Tombor B, Albert R, Oltvai ZN, BarabÃąsi A-L (2000) The large-scale organization of metabolic networks. Nature 400:107

    Google Scholar 

  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. Kossinets G, Watts DJ (2006) Empirical analysis of an evolving social network. Science 311:88–90

    Article  MathSciNet  MATH  Google Scholar 

  27. Krishnamurty B, Willinger W, Gill P, Arlitt M (2011) A Socratic method for validation of measurement-based network research. Comput Commun 34(1):43–53

    Article  Google Scholar 

  28. Marsden PV (1990) Network data and measurement. Annu Rev Soc 16:435–463

    Article  Google Scholar 

  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 305:1107d

    Article  Google Scholar 

  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 303:1538–1542

    Article  Google Scholar 

  31. Morris M (1993) Telling tails explain the discrepancy in sexual partner reports. Nature 365:437–440

    Article  Google Scholar 

  32. Newman MEJ (2004) Analysis of weighted networks. Phys Rev E 70(5):056131

    Google Scholar 

  33. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113

    Article  Google Scholar 

  34. Padgett JF, Ansell CK (1993) Robust action and the rise of the medici, 1400–1434. Amer J Sociol 98(6):1259–1319

    Article  Google Scholar 

  35. Pastor-Satorras R, Vespignani A (2001) Epidemic spreading in scale-free networks. Phys Rev Lett 86(4):3200–3203

    Article  Google Scholar 

  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):198701

    Google Scholar 

  37. Quintane E, Kleinbaum AM (2011) Matter over mind? e-mail data and the measurement of social networks. Connections 31:22–46

    Google Scholar 

  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):e1001109

    Google Scholar 

  39. Russell Bernard H, Killworth PD, Sailer L (1981) Summary of research on informant accuracy in network data. Connections 4(3):11–25

    Google Scholar 

  40. Russell Bernard H, Shelley GA, Killworth P (1987) How much of a network does the GSS and RSW dredge up? Soc Netw 9:49–61

    Google Scholar 

  41. Uzzi B, Spiro J (2005) Collaboration and creativity: the small world problem. AJS 111(2):447–504

    Google Scholar 

  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. Willinger W, Alderson D, Doyle JC (2009) Mathematics and the internet: a source of enormous confusion and great potential. Not AMS 56(5):586–599

    Google Scholar 

  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 205–218

    Google Scholar 

  45. Zweig KA (2011) Good versus optimal: why network analytic methods need more systematic evaluation. Open Comput Sci 1:137–153

    Article  Google Scholar 

  46. Zweig KA (2016) Towards a theoretical framework for analyzing complex linguistic networks. Are word-adjacency networks networks? Springer, Heidelberg, pp 153–163

    Google Scholar 

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Authors and Affiliations

  1. TU Kaiserslautern, FB Computer Science, Graph Theory and Analysis of Complex Networks, Gottlieb-Daimler-Str. 48, 67331, Kaiserslautern, Germany

    Katharina A. Zweig

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  1. Katharina A. Zweig
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Correspondence to Katharina A. Zweig .

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Zweig, K.A. (2016). Literacy: Relationships and Relations. In: Network Analysis Literacy. Lecture Notes in Social Networks. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0741-6_11

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  • DOI: https://doi.org/10.1007/978-3-7091-0741-6_11

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