Belief-propagation algorithm and the Ising model on networks with arbitrary distributions of motifs

TL;DR

This paper extends the belief-propagation algorithm to complex networks with arbitrary motifs, providing exact solutions for the Ising model and critical phenomena on these networks, revealing how motifs influence phase transitions.

Contribution

It introduces a generalized belief-propagation approach for hypergraph-structured networks with motifs, enabling exact analysis of the Ising model on loopy networks.

Findings

Exact critical temperature for ferromagnetic phase transition derived.

Critical temperature increases with clustering coefficient and loop size.

Provides the birth point of the giant connected component in loopy networks.

Abstract

We generalize the belief-propagation algorithm to sparse random networks with arbitrary distributions of motifs (triangles, loops, etc.). Each vertex in these networks belongs to a given set of motifs (generalization of the configuration model). These networks can be treated as sparse uncorrelated hypergraphs in which hyperedges represent motifs. Here a hypergraph is a generalization of a graph, where a hyperedge can connect any number of vertices. These uncorrelated hypergraphs are tree-like (hypertrees), which crucially simplify the problem and allow us to apply the belief-propagation algorithm to these loopy networks with arbitrary motifs. As natural examples, we consider motifs in the form of finite loops and cliques. We apply the belief-propagation algorithm to the ferromagnetic Ising model on the resulting random networks. We obtain an exact solution of this model on networks with finite loops or cliques as motifs. We find an exact critical temperature of the ferromagnetic phase transition and demonstrate that with increasing the clustering coefficient and the loop size, the critical temperature increases compared to ordinary tree-like complex networks. Our solution also gives the birth point of the giant connected component in these loopy networks.