Belief propagation for finite networks using a symmetry-breaking source node
Year: 2026
Type: article
Abstract: 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 treelike ones, at no additional computational cost.
Source: Physical Review Research
DOI: 10.1103/pgxg-mhjr
Authors: Seongmin Kim, Alec Kirkley
Institution: University of Hong Kong
Language: English
Subfield: Condensed Matter Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,625
OpenAlex ID: W4416246592