Approximation algorithms for orienting mixed graphs

Michael Elberfeld, Danny Segev, Colin R. Davidson, Dana Silverbush, Roded Sharan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageAmerican English
Title of host publicationCombinatorial Pattern Matching - 22nd Annual Symposium, CPM 2011, Proceedings
Pages416-428
Number of pages13
DOIs
StatePublished - 2011
Event22nd Annual Symposium on Combinatorial Pattern Matching, CPM 2011 - Palermo, Italy
Duration: 27 Jun 201129 Jun 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6661 LNCS

Conference

Conference22nd Annual Symposium on Combinatorial Pattern Matching, CPM 2011
Country/TerritoryItaly
CityPalermo
Period27/06/1129/06/11

Keywords

  • approximation algorithm
  • graph orientation
  • mixed graph
  • protein-protein interaction network

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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