Impact of Learning on the Structural Properties of Neural Networks

  • Branko Šter
  • Ivan Gabrijel
  • Andrej Dobnikar
Conference paper

DOI: 10.1007/978-3-540-71629-7_8

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4432)
Cite this paper as:
Šter B., Gabrijel I., Dobnikar A. (2007) Impact of Learning on the Structural Properties of Neural Networks. In: Beliczynski B., Dzielinski A., Iwanowski M., Ribeiro B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg

Abstract

We research the impact of the learning process of neural networks (NN) on the structural properties of the derived graphs. A type of recurrent neural network is used (GARNN). A graph is derived from a NN by defining a connection between any pair od nodes having weights in both directions above a certain threshold. We measured structural properties of graphs such as characteristic path lengths (L), clustering coefficients (C) and degree distributions (P). We found that well trained networks differ from badly trained ones in both L and C.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Branko Šter
    • 1
  • Ivan Gabrijel
    • 1
  • Andrej Dobnikar
    • 1
  1. 1.Faculty of Computer and Information Science, University of Ljubljana, Tržaška 25, 1000 LjubljanaSlovenia

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