Belief propagation for finite networks using a symmetry-breaking source node

Fuente: arXiv
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Main Authors: Kim, Seongmin, Kirkley, Alec
Format: Preprint
Published: 2025
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author Kim, Seongmin
Kirkley, Alec
author_facet Kim, Seongmin
Kirkley, Alec
contents Belief Propagation (BP) is an efficient message-passing algorithm widely used for inference in graphical models and for solving various problems in statistical physics. However, BP often yields inaccurate estimates of order parameters and their susceptibilities in finite systems, particularly in sparse networks with few loops. Here, we show for both percolation and Ising models that fixing the state of a single well-connected "source" node to break global symmetry substantially improves inference accuracy and captures finite-size effects across a broad range of networks, especially tree-like ones, at no additional computational cost.
format Preprint
id arxiv_https___arxiv_org_abs_2510_19231
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Belief propagation for finite networks using a symmetry-breaking source node
Kim, Seongmin
Kirkley, Alec
Social and Information Networks
Statistical Mechanics
Belief Propagation (BP) is an efficient message-passing algorithm widely used for inference in graphical models and for solving various problems in statistical physics. However, BP often yields inaccurate estimates of order parameters and their susceptibilities in finite systems, particularly in sparse networks with few loops. Here, we show for both percolation and Ising models that fixing the state of a single well-connected "source" node to break global symmetry substantially improves inference accuracy and captures finite-size effects across a broad range of networks, especially tree-like ones, at no additional computational cost.
title Belief propagation for finite networks using a symmetry-breaking source node
topic Social and Information Networks
Statistical Mechanics
url https://arxiv.org/abs/2510.19231