Aligning graphs and finding substructures by a cavity approach

TL;DR

This paper presents a novel distributed cavity-based algorithm for graph alignment and substructure detection, capable of analyzing large graphs and applied to biological protein interaction networks.

Contribution

It introduces a new cavity method-based algorithm for graph alignment and substructure detection, with demonstrated applications in biological network analysis.

Findings

Effective in aligning large similarity graphs

Successfully predicts protein interactions

Applicable to biological and other large-scale graphs

Abstract

We introduce a new distributed algorithm for aligning graphs or finding substructures within a given graph. It is based on the cavity method and is used to study the maximum-clique and the graph-alignment problems in random graphs. The algorithm allows to analyze large graphs and may find applications in fields such as computational biology. As a proof of concept we use our algorithm to align the similarity graphs of two interacting protein families involved in bacterial signal transduction, and to predict actually interacting protein partners between these families.