Belief propagation for supply networks: Efficient clustering of their factor graphs

TL;DR

This paper introduces a systematic clustering method for factor graphs in supply networks, enhancing belief propagation accuracy and efficiency by eliminating problematic loops, demonstrated on power grids and applicable to other flow networks.

Contribution

It proposes a novel loop-clustering approach for factor graphs that improves belief propagation performance in supply networks, outperforming existing methods.

Findings

Clustering loops in factor graphs improves BP accuracy.

The method is computationally efficient and scalable.

Demonstrated effectiveness on power grid models.

Abstract

We consider belief propagation (BP) as an efficient and scalable tool for state estimation and optimization problems in supply networks such as power grids. BP algorithms make use of factor graph representations, whose assignment to the problem of interest is not unique. It depends on the state variables and their mutual interdependencies. Many short loops in factor graphs may impede the accuracy of BP. We propose a systematic way to cluster loops of naively assigned factor graphs such that the resulting transformed factor graphs have no additional loops as compared to the original network. They guarantee an accurate performance of BP with only slightly increased computational effort, as we demonstrate by a concrete and realistic implementation for power grids. The method outperforms existing alternatives to handle the loops. We point to other applications to supply networks such as gas-pipeline or other flow networks that share the structure of constraints in the form of analogues to Kirchhoff's laws. Whenever small and abundant loops in factor graphs are systematically generated by constraints between variables in the original network, our factor-graph assignment in BP complements other approaches. It provides a fast and reliable algorithm to perform marginalization in tasks like state determination, estimation, or optimization issues in supply networks.