Tailored graph ensembles as proxies or null models for real networks II: results on directed graphs

E. Roberts, T. Schlitt, A. Coolen

Journal of Physics A 44, 275002 (2011)

#graphtheory #machinelearning #statisticalphysics

Our approach gives a rigorous quantitative method for prioritising network properties.

We generate new mathematical tools with which to quantify the macroscopic topological structure of large directed networks. This is achieved via a statistical mechanical analysis of constrained maximum entropy ensembles of directed random graphs with prescribed joint distributions for in- and out-degrees and prescribed degree–degree correlation functions. We calculate exact and explicit formulae for the leading orders in the system size of the Shannon entropies and complexities of these ensembles, and for information-theoretic distances. The results are applied to data on gene regulation networks.