PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Heat Flow-Thermodynamic Depth Complexity in Networks
Francisco Escolano, Miguel A. Lozano and Edwin Hancock
In: ICPR 2010, 23-26 Aug 2010, Istanbul, Turkey.

Abstract

In this paper we establish a formal link between network complexity in terms of Birkhoff-von Newmann decompositions and heat flow complexity (in terms of quantifying the heat flowing through the network at a given inverse temperature). Furthermore, we also define heat flow complexity in terms of thermodynamic depth, which results in a novel approach for characterizing networks and quantify their complexity. In our experiments we characterize several protein-protein interaction (PPI) networks and then highlight their evolutive differences.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:7351
Deposited By:Edwin Hancock
Deposited On:17 March 2011