Resistance distance distribution in large sparse random graphs

• Published in: Journal of statistical mechanics 2022-03, Vol.2022 (3), p.33404
• Main Authors: Akara-pipattana, Pawat, Chotibut, Thiparat, Evnin, Oleg
• Format: Article
• Language: English
• Subjects: random graphs, networks; random matrix theory and extensions
• ISSN: 1742-5468; 1742-5468
• DOI: 10.1088/1742-5468/ac57ba

We consider an Erdős–Rényi random graph consisting of N vertices connected by randomly and independently drawing an edge between every pair of them with probability c / N so that at N → ∞ one obtains a graph of finite mean degree c . In this regime, we study the distribution of resistance distances between the vertices of this graph and develop an auxiliary field representation for this quantity in the spirit of statistical field theory. Using this representation, a saddle point evaluation of the resistance distance distribution is possible at N → ∞ in terms of an 1/ c expansion. The leading order of this expansion captures the results of numerical simulations very well down to rather small values of c ; for example, it recovers the empirical distribution at c = 4 or 6 with an overlap of around 90%. At large values of c , the distribution tends to a Gaussian of mean 2/ c and standard deviation 2 / c 3 . At small values of c , the distribution is skewed toward larger values, as captured by our saddle point analysis, and many fine features appear in addition to the main peak, including subleading peaks that can be traced back to resistance distances between vertices of specific low degrees and the rest of the graph. We develop a more refined saddle point scheme that extracts the corresponding degree-differentiated resistance distance distributions. We then use this approach to recover analytically the most apparent of the subleading peaks that originates from vertices of degree 1. Rather intuitively, this subleading peak turns out to be a copy of the main peak, shifted by one unit of resistance distance and scaled down by the probability for a vertex to have degree 1. We comment on a possible lack of smoothness in the true N → ∞ distribution suggested by the numerics.