Title
Thermodynamic characterization of networks using graph polynomials.
Abstract
In this paper, we present a method for characterizing the evolution of time-varying complex networks by adopting a thermodynamic representation of network structure computed from a polynomial (or algebraic) characterization of graph structure. Commencing from a representation of graph structure based on a characteristic polynomial computed from the normalized Laplacian matrix, we show how the polynomial is linked to the Boltzmann partition function of a network. This allows us to compute a number of thermodynamic quantities for the network, including the average energy and entropy. Assuming that the system does not change volume, we can also compute the temperature, defined as the rate of change of entropy with energy. All three thermodynamic variables can be approximated using low-order Taylor series that can be computed using the traces of powers of the Laplacian matrix, avoiding explicit computation of the normalized Laplacian spectrum. These polynomial approximations allow a smoothed representation of the evolution of networks to be constructed in the thermodynamic space spanned by entropy, energy, and temperature. We show how these thermodynamic variables can be computed in terms of simple network characteristics, e.g., the total number of nodes and node degree statistics for nodes connected by edges. We apply the resulting thermodynamic characterization to real-world time-varying networks representing complex systems in the financial and biological domains. The study demonstrates that the method provides an efficient tool for detecting abrupt changes and characterizing different stages in network evolution.
Year
DOI
Venue
2015
10.1103/PhysRevE.92.032810
PHYSICAL REVIEW E
Field
DocType
Volume
Characteristic polynomial,Laplacian matrix,Discrete mathematics,Applied mathematics,Entropy (energy dispersal),Algebraic number,Polynomial,Partition function (statistical mechanics),Complex network,Classical mechanics,Mathematics,Taylor series
Journal
92
Issue
ISSN
Citations 
3
1539-3755
11
PageRank 
References 
Authors
0.95
0
8