Constraints and entropy in a model of network evolution

Philip Tee (), Ian Wakeman, George Parisis, Jonathan Dawes and István Z. Kiss
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Philip Tee: Moogsoft Inc
Ian Wakeman: School of Engineering and Informatics, University of Sussex
George Parisis: School of Engineering and Informatics, University of Sussex
Jonathan Dawes: Centre for Networks and Collective Behaviour, and Department of Mathematical Sciences, University of Bath
István Z. Kiss: School of Mathematical and Physical Sciences, University of Sussex

The European Physical Journal B: Condensed Matter and Complex Systems, 2017, vol. 90, issue 11, 1-16

Abstract: Abstract Barabási–Albert’s “Scale Free” model is the starting point for much of the accepted theory of the evolution of real world communication networks. Careful comparison of the theory with a wide range of real world networks, however, indicates that the model is in some cases, only a rough approximation to the dynamical evolution of real networks. In particular, the exponent γ of the power law distribution of degree is predicted by the model to be exactly 3, whereas in a number of real world networks it has values between 1.2 and 2.9. In addition, the degree distributions of real networks exhibit cut offs at high node degree, which indicates the existence of maximal node degrees for these networks. In this paper we propose a simple extension to the “Scale Free” model, which offers better agreement with the experimental data. This improvement is satisfying, but the model still does not explain why the attachment probabilities should favor high degree nodes, or indeed how constraints arrive in non-physical networks. Using recent advances in the analysis of the entropy of graphs at the node level we propose a first principles derivation for the “Scale Free” and “constraints” model from thermodynamic principles, and demonstrate that both preferential attachment and constraints could arise as a natural consequence of the second law of thermodynamics.

Keywords: Statistical; and; Nonlinear; Physics (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1140/epjb/e2017-80185-5

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