Solving generalized maximum-weight connected subgraph problem for network enrichment analysis

TL;DR

This paper introduces the first practical exact solver for the generalized maximum-weight connected subgraph problem, which is crucial for network enrichment analysis involving both node and edge scoring.

Contribution

The paper presents a novel exact solver for the GMWCS problem, enabling more accurate network enrichment analysis by handling both node and edge weights.

Findings

GMWCS solver performs comparably to the best solvers on node-weighted instances.

It is faster than similar solvers on instances with edge weights.

The solver is effective on real-world network analysis problems.

Abstract

Network enrichment analysis methods allow to identify active modules without being biased towards a priori defined pathways. One of mathematical formulations of such analysis is a reduction to a maximum-weight connected subgraph problem. In particular, in analysis of metabolic networks a generalized maximum-weight connected subgraph (GMWCS) problem, where both nodes and edges are scored, naturally arises. Here we present the first to our knowledge practical exact GMWCS solver. We have tested it on real-world instances and compared to similar solvers. First, the results show that on node-weighted instances GMWCS solver has a similar performance to the best solver for that problem. Second, GMWCS solver is faster compared to the closest analogue when run on GMWCS instances with edge weights.