Susceptibility Propagation by Using Diagonal Consistency

TL;DR

This paper introduces an improved susceptibility propagation method for Markov random fields, combining belief propagation with a diagonal matching approach to enhance robustness and accuracy in inverse Ising problems.

Contribution

The paper proposes a novel susceptibility propagation technique using diagonal matching, improving robustness and unifying existing methods.

Findings

Enhanced robustness across various network structures

Reduces to standard susceptibility propagation in special cases

Unifies susceptibility propagation and adaptive TAP equations

Abstract

A susceptibility propagation that is constructed by combining a belief propagation and a linear response method is used for approximate computation for Markov random fields. Herein, we formulate a new, improved susceptibility propagation by using the concept of a diagonal matching method that is based on mean-field approaches to inverse Ising problems. The proposed susceptibility propagation is robust for various network structures, and it is reduced to the ordinary susceptibility propagation and to the adaptive Thouless-Anderson-Palmer equation in special cases.