Improved Approximation for Orienting Mixed Graphs

TL;DR

This paper improves approximation algorithms for the maximum mixed graph orientation problem, which is relevant in biological and communication networks, by leveraging a local-to-global orientation property and exploring problem variants.

Contribution

It introduces an improved approximation approach based on a new local-to-global orientation property and develops algorithms with better guarantees for the general problem.

Findings

Enhanced approximation ratios for the problem.

New algorithm achieving better guarantees.

Analysis of variants of the orientation problem.

Abstract

An instance of the maximum mixed graph orientation problem consists of a mixed graph and a collection of source-target vertex pairs. The objective is to orient the undirected edges of the graph so as to maximize the number of pairs that admit a directed source-target path. This problem has recently arisen in the study of biological networks, and it also has applications in communication networks. In this paper, we identify an interesting local-to-global orientation property. This property enables us to modify the best known algorithms for maximum mixed graph orientation and some of its special structured instances, due to Elberfeld et al. (CPM '11), and obtain improved approximation ratios. We further proceed by developing an algorithm that achieves an even better approximation guarantee for the general setting of the problem. Finally, we study several well-motivated variants of this orientation problem.