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Approximation Algorithms for Orienting Mixed Graphs
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BibTeX
@MISC{Elberfeld_approximationalgorithms,
author = {Michael Elberfeld and Danny Segev and Colin R. Davidson and Dana Silverbush and Roded Sharan},
title = {Approximation Algorithms for Orienting Mixed Graphs},
year = {}
}
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Abstract
Abstract. Graph orientation is a fundamental problem in graph theory that has recently arisen in the study of signaling-regulatory pathways in protein networks. Given a graph and a list of ordered source-target vertex pairs, it calls for assigning directions to the edges of the graph so as to maximize the number of pairs that admit a directed source-to-target path. When the input graph is undirected, a sub-logarithmic approximation is known for the problem. However, the approximability of the biologically-relevant variant, in which the input graph has both directed and undirected edges, was left open. Here we give the first approximation algorithm to this problem. Our algorithm provides a sub-linear guarantee in the general case, and logarithmic guarantees for structured instances. Key words: protein-protein interaction network, mixed graph, graph orientation, approximation algorithm 1
Keyphrases
orienting mixed graph approximation algorithm graph orientation input graph undirected edge graph theory sub-logarithmic approximation signaling-regulatory pathway directed source-to-target path structured instance protein network sub-linear guarantee fundamental problem logarithmic guarantee key word general case first approximation algorithm ordered source-target vertex pair mixed graph biologically-relevant variant protein-protein interaction network
