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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
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8127)

Abstract

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.

Keywords

Network Measures Graph Entropy structural information graph complexity measures structural complexity 

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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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