Network Representations of Complex Systems

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


Network analysis starts with the available data on relationships between entities of the complex system to observe. In this chapter, the main modeling decisions to turn a raw data set into a complex network are discussed.


Complex Network Bipartite Graph Social Network Analysis Network Representation Network Analytic Process 
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.
    Abe F et al (1995) Observation of top quark production in \(\overline{p}p\) collisions with the collider detector at fermilab. Phys Rev Lett 74(14):2626–2631CrossRefGoogle Scholar
  2. 2.
    Ahn YY, Ahnert SE, Bagrow JP, Barabási A-L (2011) Flavor network and the principles of food pairing. Sci Rep 1:196CrossRefGoogle Scholar
  3. 3.
    Alhajj R, Rokne J (eds) (2014) Encyclopedia of social network analysis and mining. Springer, HeidelbergzbMATHGoogle Scholar
  4. 4.
    Arita M (2004) The metabolic world of escherichia coli is not small. Proc the Natl Acad Sci 101(6):1543–1547CrossRefGoogle Scholar
  5. 5.
    Barabási A-L, Jeong H, Ravasz E, Schubert A, Vicsek T (2002) Evolution of the social network of scientific collaborations. Phys A 311:590–614MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Bonnefoy PA, John Hansman R (2007) Scalability and efficiency dynamics of air transportation networks in the United States. Technical report, Massachusetts Institute of Technology, Cambridge, MA, USAGoogle Scholar
  7. 7.
    Borgatti SP, Halgin D (2011) The SAGE handbook of social network analysis. Analyzing affiliation networks. Sage Publications, London, pp 417–433Google Scholar
  8. 8.
    Borgatti SP, Mehra A, Brass DJ, Labianca G (2009) Network analysis in the social sciences. Science 323:892–895CrossRefGoogle Scholar
  9. 9.
    Brewer DD, Webster CM (1999) Forgetting of friends and its effects on measuring friendship networks. Soc Netw 21:361–373CrossRefGoogle Scholar
  10. 10.
    Butts CT (2009) Revisiting the foundations of network analysis. Science 325(5939):414–416MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Butts CT (2010) A note on generalized edges.
  12. 12.
    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
  13. 13.
    Easley D, Kleinberg J (2010) Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University PressGoogle Scholar
  14. 14.
    Geng L, Hamilton HJ (2006) Interestingness measures for data mining: a survey. ACM Comput Surv 38(3):9CrossRefGoogle Scholar
  15. 15.
    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):e73413CrossRefGoogle Scholar
  16. 16.
    Jeong H, Tombor B, Albert R, Oltvai ZN, Barabãsi A-L (2000) The large-scale organization of metabolic networks. Nature 400:107Google Scholar
  17. 17.
    Kergl D, Roedler R, Seeber S, Dreo Rodosek G (2014) On the endogenesis of twitter’s spritzer and gardenhose sample streams. In: Proceedings of the 2014 IEEE/ACM international conference on advances in social network analysis and mining (ASONAM’14), pp 357–364Google Scholar
  18. 18.
    Latapy M, Magnien C, Del Vecchio N (2008) Basic notions for the analysis of large two-mode networks. Soc Netw 30(1):31–48CrossRefGoogle Scholar
  19. 19.
    Laumann EO, Marsden PV, Prensky D (1992) Research methods in social network analysis. The boundary specification problem in network analysis. Transaction Publishers, New Brunswick, New Jersey, pp 61–88 (reprint)Google Scholar
  20. 20.
    Lazer D, Pentland A, Adamic L, Aral S, Barabási A-L, Brewer D, Christakis NA, Contractor N, Fowler J, Gutmann M, Jebara T, King G, Macy M, Roy D, Van Alstyne M (2009) Computational social science. Science 323:721–723CrossRefGoogle Scholar
  21. 21.
  22. 22.
    Leskovec J, Huttenlocher D, Kleinberg J (2010) Predicting positive and negative links in online social networks. In: Proceedings of the 19th international conference on world wide web (WWW’10)Google Scholar
  23. 23.
    Li M, Fan Y, Chen J, Gao L, Di Z, Jinshan W (2005) Weighted networks of scientific communication: the measurement and topological role of weight. Phys A 350:643–656CrossRefGoogle Scholar
  24. 24.
    Newman MEJ (2001) Scientific collaboration networks. i. network construction and fundamental results. Phys Rev E 64:016131CrossRefGoogle Scholar
  25. 25.
    Newman MEJ (2001) Scientific collaboration networks. ii. shortest paths, weighted networks, and centrality. Phys Rev E 64:016132CrossRefGoogle Scholar
  26. 26.
    Newman MEJ (2004) Analysis of weighted networks. Phys Rev E 70(5):056131CrossRefGoogle Scholar
  27. 27.
    Prell C (2011) Social network analysis. SAGE Publications Ltd., LondonGoogle Scholar
  28. 28. “Publish or Perish” by Anne-Wil Harzing
  29. 29.
    Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabási A-L (2002) Hierarchical organization of modularity in metabolic networks. Science 297:1551–1553CrossRefGoogle Scholar
  30. 30.
    Teng C-Y, Lin Y-R, Adamic LA (2012) Recipe recommendation using ingredient networks. In Proceedings of the 3rd annual ACM web science conference (WebSci’12). ACM, New York, pp 298–307Google Scholar
  31. 31.
  32. 32.
  33. 33.
    Wasserman S, Faust K (1999) Social network analysis–methods and applications, revised, reprinted edn. Cambridge University Press, CambridgezbMATHGoogle Scholar
  34. 34.
    Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442CrossRefGoogle Scholar
  35. 35.
    Zweig KA (2014) Encyclopedia of social network analysis and mining. Network representations of complex data, Springer, HeidelbergGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria 2016

Authors and Affiliations

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

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