Cumulative Merging Percolation: A long-range percolation process in networks

TL;DR

This paper introduces Cumulative Merging Percolation (CMP), a long-range percolation process on networks that allows nodes to merge into clusters despite topological distance, revealing complex phase transitions and modeling diverse network dynamics.

Contribution

It develops a generalized CMP framework based on interaction range functions, uncovering richer phase transition behaviors and crossover phenomena not seen in previous models.

Findings

Analytic predictions match numerical simulations.

CMP exhibits complex phase transitions with competing mechanisms.

Clusters do not necessarily align with topological connected components.

Abstract

Percolation on networks is a common framework to model a wide range of processes, from cascading failures to epidemic spreading. Standard percolation assumes short-range interactions, implying that nodes can merge into clusters only if they are nearest-neighbors. Cumulative Merging Percolation (CMP) is an new percolation process that assumes long-range interactions, such that nodes can merge into clusters even if they are topologically distant. Hence in CMP percolation clusters do not coincide with the topological connected components of the network. Previous work has shown that a specific formulation of CMP features peculiar mechanisms for the formation of the giant cluster, and allows to model different network dynamics such as recurrent epidemic processes. Here we develop a more general formulation of CMP in terms of the functional form of the cluster interaction range, showing an even richer phase transition scenario with competition of different mechanisms resulting in crossover phenomena. Our analytic predictions are confirmed by numerical simulations.