On Graph Entropy Measures for Knowledge Discovery from Publication Network Data

  • Andreas Holzinger
  • Bernhard Ofner
  • Christof Stocker
  • André Calero Valdez
  • Anne Kathrin Schaar
  • Martina Ziefle
  • Matthias Dehmer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8127)


Many research problems are extremely complex, making interdisciplinary knowledge a necessity; consequently cooperative work in mixed teams is a common and increasing research procedure. In this paper, we evaluated information-theoretic network measures on publication networks. For the experiments described in this paper we used the network of excellence from the RWTH Aachen University, described in [1]. Those measures can be understood as graph complexity measures, which evaluate the structural complexity based on the corresponding concept. We see that it is challenging to generalize such results towards different measures as every measure captures structural information differently and, hence, leads to a different entropy value. This calls for exploring the structural interpretation of a graph measure [2] which has been a challenging problem.


Network Measures Graph Entropy structural information graph complexity measures structural complexity 


  1. 1.
    Calero Valdez, A., Schaar, A.K., Ziefle, M., Holzinger, A., Jeschke, S., Brecher, C.: Using mixed node publication network graphs for analyzing success in interdisciplinary teams. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds.) AMT 2012. LNCS, vol. 7669, pp. 606–617. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Dehmer, M.: Information theory of networks. Symmetry 3(4), 767–779 (2011)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Merton, R.: The Matthew Effect in Science: The reward and communication systems of science are considered. Science 159(3810), 56–63 (1968)CrossRefGoogle Scholar
  4. 4.
    Wuchty, S., Jones, B., Uzzi, B.: The increasing dominance of teams in production of knowledge. Science 316(5827), 1036–1039 (2007)CrossRefGoogle Scholar
  5. 5.
    Holzinger, A.: Successful Management of Research Development. BoD–Books on Demand (2011)Google Scholar
  6. 6.
    Dehmer, M.: Information processing in complex networks: Graph entropy and information functionals. Appl. Math. Comput. 201(1-2), 82–94 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Dehmer, M., Varmuza, K., Borgert, S., Emmert-Streib, F.: On entropy-based molecular descriptors: Statistical analysis of real and synthetic chemical structures. Journal of Chemical Information and Modeling 49, 1655–1663 (2009)CrossRefGoogle Scholar
  8. 8.
    Dehmer, M., Mowshowitz, A.: A history of graph entropy measures. Inf. Sci. 181(1), 57–78 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Holzinger, A., Stocker, C., Bruschi, M., Auinger, A., Silva, H., Gamboa, H., Fred, A.: On Applying Approximate Entropy to ECG Signals for Knowledge Discovery on the Example of Big Sensor Data. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds.) AMT 2012. LNCS, vol. 7669, pp. 646–657. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Calero Valdez, A., Ziefle, M., Alagöz, F., Holzinger, A.: Mental models of menu structures in diabetes assistants. In: Miesenberger, K., Klaus, J., Zagler, W., Karshmer, A. (eds.) ICCHP 2010, Part II. LNCS, vol. 6180, pp. 584–591. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Salotti, J., Plantevit, M., Robardet, C., Boulicaut, J.F.: Supporting the Discovery of Relevant Topological Patterns in Attributed Graphs (December 2012), Demo Session of the IEEE International Conference on Data Mining (IEEE ICDM 2012)Google Scholar
  12. 12.
    Shannon, C.E.: A mathematical theory of communication. Bell System Technical Journal 27 (1948)Google Scholar
  13. 13.
    Mowshowitz, A.: Entropy and the complexity of graphs: I. An index of the relative complexity of a graph 30, 175–204 (1968)Google Scholar
  14. 14.
    Holzinger, A., Stocker, C., Peischl, B., Simonic, K.M.: On using entropy for enhancing handwriting preprocessing. Entropy 14(11), 2324–2350 (2012)CrossRefGoogle Scholar
  15. 15.
    Mowshowitz, A., Dehmer, M.: Entropy and the complexity of graphs revisited. Entropy 14(3), 559–570 (2012)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Solé, R., Valverde, S.: Information Theory of Complex Networks: On Evolution and Architectural Constraints. In: Ben-Naim, E., Frauenfelder, H., Toroczkai, Z. (eds.) Complex Networks. Lecture Notes in Physics, vol. 650, pp. 189–207. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Ji, L., Bing-Hong, W., Wen-Xu, W., Tao, Z.: Network entropy based on topology configuration and its computation to random networks. Chinese Physics Letters 25(11), 4177 (2008)CrossRefGoogle Scholar
  18. 18.
    Jooss, C., Welter, F., Leisten, I., Richert, A., Schaar, A., Calero Valdez, A., Nick, E., Prahl, U., Jansen, U., Schulz, W., et al.: Scientific cooperation engineering in the cluster of excellence integrative production technology for high-wage countries at rwth aachen university. In: ICERI 2012 Proceedings, pp. 3842–3846 (2012)Google Scholar
  19. 19.
    Schaar, A.K., Calero Valdez, A., Ziefle, M.: Publication network visualisation as an approach for interdisciplinary innovation management. In: IEEE Professional Communication Conference (IPCC) (2013)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Andreas Holzinger
    • 1
  • Bernhard Ofner
    • 1
  • Christof Stocker
    • 1
  • André Calero Valdez
    • 2
  • Anne Kathrin Schaar
    • 2
  • Martina Ziefle
    • 2
  • Matthias Dehmer
    • 3
  1. 1.Institute for Medical Informatics, Statistics & Documentation, Research Unit Human-Computer InteractionMedical University GrazGrazAustria
  2. 2.Human-Computer Interaction CenterRWTH Aachen UniversityGermany
  3. 3.Institute for Bioinformatics and Translational ResearchUMIT TyrolAustria

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