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Heat Flow-Thermodynamic Depth Complexity in Directed Networks

  • Francisco Escolano
  • Boyan Bonev
  • Edwin R. Hancock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)

Abstract

In this paper we extend the heat diffusion-thermodynamic depth approach for undirected networks/graphs to directed graphs. This extension is motivated by the need to measure the complexity of structural patterns encoded by directed graphs. It consists of: a) analyzing and characterizing heat diffusion traces in directed graphs, b) extending the thermodynamic depth framework to capture the second-order variability of the diffusion traces to measure the complexity of directed networks. In our experiments we characterize several directed networks derived from different natural languages. We show that our proposed extension finds differences between languages that are blind to the classical analysis of degree distributions.

Keywords

Heat Kernel Degree Distribution Directed Network Coverage Ratio Parallel Corpus 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Francisco Escolano
    • 1
  • Boyan Bonev
    • 1
  • Edwin R. Hancock
    • 2
  1. 1.University of AlicanteSpain
  2. 2.University of YorkUK

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