Clustering and percolation on superpositions of Bernoulli random graphs

Abstract

A simple but powerful network model with $n$ nodes and $m$ partly overlapping layers is generated as an overlay of independent random graphs $G_1,\dots ,G_m$ with variable sizes and densities. The model is parameterised by a joint distribution $P_n$ of layer sizes and densities. When $m$ grows linearly and $P_n\to P$ as $n\to \infty$, the model generates sparse random graphs with a rich statistical structure, admitting a nonvanishing clustering coefficient together with a limiting degree distribution and clustering spectrum with tunable power-law exponents. Remarkably, the model admits parameter regimes in which bond percolation exhibits two phase transitions: the first related to the emergence of a giant connected component, and the second to the appearance of gigantic single-layer components.

The article has been rewritten with updated notations, updated proofs, and added figures