Publication Network Analysis of an Academic Family in Information Systems

  • Jörn Grahl
  • Bastian Sand
  • Michael Schneider
  • Michael Schwind


The study of scientific collaboration through network analysis can give interesting conclusions about the publication habits of a scientific community. Co-authorship networks represent scientific collaboration as a graph: nodes correspond to authors, edges between nodes mark joint publications (Newman 2001a,b). Scientific publishing is decentralized. Choices of co-authors and research topics are seldomly globally coordinated. Still, the structure of co-authorship networks is far from random. Co-authorship networks are governed by principles that are similar in other complex networks such as social networks (Wasserman and Faust 1994), networks of citations between scientific papers (Egghe and Rousseau 1990), the World Wide Web (Albert and Barabási 1999, Kleinberg et al. 1999) or power grids (Watts and Strogatz 1998). It is therefore not astounding that scholars have studied co-authorship networks in considerable detail and in a variety of contexts, such as physics (Newman 2001a), evolutionary computation (Cotta and Merelo 2007) or computer supported cooperative work (Horn et al. 2004).


Centrality Measure Betweenness Centrality Community Detection Scientific Collaboration 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.


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  1. Albert, R., Jeong, H. and Barabási, A.-L. “Internet: Diameter of the world-wide web”, Nature (401), 1999, pp. 130–131.CrossRefGoogle Scholar
  2. Barabási, A.-L. and Albert, R. “Emergence of scaling in random networks”, Science (286), 1999, pp. 509–512.CrossRefGoogle Scholar
  3. Beauchamp, M. A. “An improved index of centrality”, Behavioral Science (10:2), 1965, pp. 161–163.CrossRefGoogle Scholar
  4. Blondel, V. D., Guillaume, J.-L., Lambiotte, R. and Lefebvre, E. “Fast unfolding of communities in large networks”, Journal of Statistical Mechanics: Theory and Experiment (10), 2008, P10008.CrossRefGoogle Scholar
  5. Bonacich, P. and Lloyd, P. “Eigenvector-like measures of centrality for asymmetric relations”, Social Networks (23:3), 2001, pp. 191–201.CrossRefGoogle Scholar
  6. Brin, S. and Page, L. “The anatomy of a large-scale hypertextual web search engine”, Computer networks and ISDN systems (30), 1998, pp. 107–117.CrossRefGoogle Scholar
  7. Cohen, R., Erez, K., Ben Avraham, D. and Havlin, S. “Resiliance of the internet to random breakdowns”, Physical Review Letters (85:21), 2000, pp. 4626–4628.CrossRefGoogle Scholar
  8. Cotta, C. and Merelo, J. J. “Where is evolutionary computation going? A temporal analysis of the EC community”, Genetic programming and evolvable machines (8:3), 2007, pp. 239–253.CrossRefGoogle Scholar
  9. Egghe, L., and Rousseau, R. Introduction to Infometrics, Elsevier, Amsterdam, 1990.Google Scholar
  10. Everett, M. G. and Borgatti, S. P. “The centrality of groups and classes”, Journal of Mathematical Sociology (23:3), 1999, pp. 181–201.CrossRefGoogle Scholar
  11. Freeman. L. C. “A set of measures of centrality based on betweenness”, Sociometry (40:1), 1977, pp. 35–41.CrossRefGoogle Scholar
  12. Horn, D. B., Finholt, T. A., Birnholtz, J. P., Motwani, D. and Jayaraman, S. “The six degrees of Jonathan Grudin: A social network analysis of the evolution and impact of CSCW research”, In Proceedings of the 2004 ACM Conference on Computer supported cooperative work (CSCW ’04), 2004, pp. 582–591.Google Scholar
  13. Katz, L. “A new status index derived from sociometric analysis”, Psychometrika (18:1), 1953, pp. 39–43.CrossRefGoogle Scholar
  14. Kleinberg, J. M., Kumar, S. R., Raghavan, P., Rajagopalan, S. and Tomkins A. “The Web as a graph: Measurements, models and methods”, In Lecture Notes in Computer Science (1627), 1999, pp. 1–18.Google Scholar
  15. Kurbel, K., Brenner, W., Chamoni, P., Frank, U., Mertens, P. and Roithmayr, F. Studienführer Wirtschaftsinformatik 2009/2010. Studieninhalte-Anwendungsfelder-Berufsbilder. Universitäten in Deutschland/Österreich/Schweiz. Gabler, 2008.Google Scholar
  16. Landherr, A., Friedl, B. and Heidemann, J. “A critical review of centrality measures in social networks”, Business & Information Systems Engineering (2:6), 2010, pp. 371–385.CrossRefGoogle Scholar
  17. Milgram, S. “The Small World Problem”, Psychology Today (2), 1967, pp. 60–67.Google Scholar
  18. Newman, M. E. J. “Scientific collaboration networks. I. Network construction and fundamental results”, Physical Review E (64:1), 2001a, pp. 06131–1 –06131–8.Google Scholar
  19. Newman, M. E. J. “Scientific collaboration networks. II. Shortest paths, weighted networks, centrality”, Physical Review E (64:1), 2001b, pp. 06132–1 – 06132–7.Google Scholar
  20. Nieminen, J. “On the centrality in a graph”, Scandinavian Journal of Psychology (15:1), 1974, pp. 332–336.CrossRefGoogle Scholar
  21. Ravasz, E. and Barabási, A.-L. “Hierarchical organization in complex networks”, Physical Review E (67:2), 2003, pp. 026112–1 – 026112–7.CrossRefGoogle Scholar
  22. Shaw, M. E. “Group structure and the behavior of individuals in small groups”, Journal of Psychology (38), 1954, pp. 139–149.CrossRefGoogle Scholar
  23. Wagner, C. S. and Leydesdorff, L. “Network structure, self-organization, and the growth of international collaboration in science”, Research Policy (34:10), 2005, pp. 1608–1618.CrossRefGoogle Scholar
  24. Wasserman, S. and Faust, K. Social network analysis: Methods and applications, Cambridge University Press, 1994.Google Scholar
  25. Watts, D. J. and Strogatz, S. H. “Collective dynamics of small-world networks”, Nature (393), 1998, pp. 440–442.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jörn Grahl
    • 1
  • Bastian Sand
    • 2
  • Michael Schneider
    • 2
  • Michael Schwind
    • 3
  1. 1.Information Systems and Business AdministrationJohannes Gutenberg-University MainzMainzGermany
  2. 2.Business Information Systems & Operations ResearchUniversity of KaiserslauternKaiserslauternGermany
  3. 3.IT-based LogisticsGoethe-University FrankfurtFrankfurtGermany

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