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 languageEnglish
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
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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

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