Percolation transition in correlated hypergraphs

Abstract

Correlations are known to play a crucial role in determining the structure of complex networks. Here we study how their presence affects the computation of the percolation threshold in random hypergraphs. In order to mimic the correlation in real network, we build hypergraphs from a generalized hidden variable ensembles and we study the percolation transition by mapping this problem to the fully connected Potts model with heterogeneous couplings.

References in corpus (17)

• Statistical mechanics of complex networks
• The structure and function of complex networks
• Error and attack tolerance of complex networks
• Resilience of the Internet to random breakdowns
• Critical phenomena in complex networks
• Breakdown of the Internet under intentional attack
• Universal Behavior of Load Distribution in Scale-free Networks
• The statistical mechanics of networks
• Class of correlated random networks with hidden variables
• The entropy of network ensembles
• The entropy of randomized network ensembles
• Generalized percolation in random directed networks
• Percolation on correlated networks
• Evolution of scale-free random graphs: Potts model formulation
• Bicomponents and the robustness of networks to failure
• On large deviation properties of Erdos-Renyi random graphs
• Percolation transition and distribution of connected components in generalized random network ensembles

Cited by in corpus (7)

• Networks beyond pairwise interactions: structure and dynamics
• Percolation on complex networks: Theory and application
• Higher-order percolation processes on multiplex hypergraphs
• Analysis and perturbation of degree correlation in complex networks
• Weighted projected networks: mapping hypergraphs to networks
• Prevalence of multistability and nonstationarity in driven chemical networks
• The distribution of the number of node neighbors in random hypergraphs