Approximation algorithms for orienting mixed graphs

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

doi:10.1007/978-3-642-21458-5_35

Approximation algorithms for orienting mixed graphs

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

Department of Statistics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Combinatorial Pattern Matching - 22nd Annual Symposium, CPM 2011, Proceedings
Pages	416-428
Number of pages	13
DOIs	https://doi.org/10.1007/978-3-642-21458-5_35
State	Published - 2011
Event	22nd Annual Symposium on Combinatorial Pattern Matching, CPM 2011 - Palermo, Italy Duration: 27 Jun 2011 → 29 Jun 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6661 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd Annual Symposium on Combinatorial Pattern Matching, CPM 2011
Country/Territory	Italy
City	Palermo
Period	27/06/11 → 29/06/11

Bibliographical note

Funding Information:
M.E. was supported by a research grant from the Dr. Alexander und Rita Besser-Stiftung. C.R.D. would like to thank Gerry Schwartz, Heather Reisman, and the University of Waterloo-Haifa International Experience Program for funding his visit to the University of Haifa, during which part of this work was done. R.S. was supported by a research grant from the Israel Science Foundation (grant no. 241/11).

Keywords

approximation algorithm
graph orientation
mixed graph
protein-protein interaction network

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-21458-5_35

Cite this

Elberfeld, M., Segev, D., Davidson, C. R., Silverbush, D., & Sharan, R. (2011). Approximation algorithms for orienting mixed graphs. In Combinatorial Pattern Matching - 22nd Annual Symposium, CPM 2011, Proceedings (pp. 416-428). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6661 LNCS). https://doi.org/10.1007/978-3-642-21458-5_35

@inproceedings{4d9ee0d5a9e6449999ec3954d8ff1d68,

title = "Approximation algorithms for orienting mixed graphs",

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.",

keywords = "approximation algorithm, graph orientation, mixed graph, protein-protein interaction network",

author = "Michael Elberfeld and Danny Segev and Davidson, {Colin R.} and Dana Silverbush and Roded Sharan",

note = "Funding Information: M.E. was supported by a research grant from the Dr. Alexander und Rita Besser-Stiftung. C.R.D. would like to thank Gerry Schwartz, Heather Reisman, and the University of Waterloo-Haifa International Experience Program for funding his visit to the University of Haifa, during which part of this work was done. R.S. was supported by a research grant from the Israel Science Foundation (grant no. 241/11).; 22nd Annual Symposium on Combinatorial Pattern Matching, CPM 2011 ; Conference date: 27-06-2011 Through 29-06-2011",

year = "2011",

doi = "10.1007/978-3-642-21458-5_35",

language = "English",

isbn = "9783642214578",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "416--428",

booktitle = "Combinatorial Pattern Matching - 22nd Annual Symposium, CPM 2011, Proceedings",

}

Elberfeld, M, Segev, D, Davidson, CR, Silverbush, D & Sharan, R 2011, Approximation algorithms for orienting mixed graphs. in Combinatorial Pattern Matching - 22nd Annual Symposium, CPM 2011, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6661 LNCS, pp. 416-428, 22nd Annual Symposium on Combinatorial Pattern Matching, CPM 2011, Palermo, Italy, 27/06/11. https://doi.org/10.1007/978-3-642-21458-5_35

Approximation algorithms for orienting mixed graphs. / Elberfeld, Michael; Segev, Danny; Davidson, Colin R. et al.
Combinatorial Pattern Matching - 22nd Annual Symposium, CPM 2011, Proceedings. 2011. p. 416-428 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6661 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Approximation algorithms for orienting mixed graphs

AU - Elberfeld, Michael

AU - Segev, Danny

AU - Davidson, Colin R.

AU - Silverbush, Dana

AU - Sharan, Roded

N1 - Funding Information: M.E. was supported by a research grant from the Dr. Alexander und Rita Besser-Stiftung. C.R.D. would like to thank Gerry Schwartz, Heather Reisman, and the University of Waterloo-Haifa International Experience Program for funding his visit to the University of Haifa, during which part of this work was done. R.S. was supported by a research grant from the Israel Science Foundation (grant no. 241/11).

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - approximation algorithm

KW - graph orientation

KW - mixed graph

KW - protein-protein interaction network

UR - http://www.scopus.com/inward/record.url?scp=79960100533&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-21458-5_35

DO - 10.1007/978-3-642-21458-5_35

M3 - Conference contribution

AN - SCOPUS:79960100533

SN - 9783642214578

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 416

EP - 428

BT - Combinatorial Pattern Matching - 22nd Annual Symposium, CPM 2011, Proceedings

T2 - 22nd Annual Symposium on Combinatorial Pattern Matching, CPM 2011

Y2 - 27 June 2011 through 29 June 2011

ER -

Elberfeld M, Segev D, Davidson CR, Silverbush D, Sharan R. Approximation algorithms for orienting mixed graphs. In Combinatorial Pattern Matching - 22nd Annual Symposium, CPM 2011, Proceedings. 2011. p. 416-428. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-21458-5_35

Approximation algorithms for orienting mixed graphs

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this