Better decomposition heuristics for the maximum-weight connected graph problem using betweenness centrality

Takanori Yamamoto; Hideo Bannai; Masao Nagasaki; Satoru Miyano

doi:10.1007/978-3-642-04747-3_40

Better decomposition heuristics for the maximum-weight connected graph problem using betweenness centrality

Takanori Yamamoto, Hideo Bannai, Masao Nagasaki, Satoru Miyano

Mathematical Informatics

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

6 Citations (Scopus)

Abstract

We present new decomposition heuristics for finding the optimal solution for the maximum-weight connected graph problem, which is known to be NP-hard. Previous optimal algorithms for solving the problem decompose the input graph into subgraphs using heuristics based on node degree. We propose new heuristics based on betweenness centrality measures, and show through computational experiments that our new heuristics tend to reduce the number of subgraphs in the decomposition, and therefore could lead to the reduction in computational time for finding the optimal solution. The method is further applied to analysis of biological pathway data.

Original language	English
Title of host publication	Discovery Science - 12th International Conference, DS 2009, Proceedings
Pages	465-472
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-04747-3_40
Publication status	Published - 2009
Event	12th International Conference on Discovery Science, DS 2009 - Porto, Portugal Duration: Oct 3 2009 → Oct 5 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5808 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	12th International Conference on Discovery Science, DS 2009
Country/Territory	Portugal
City	Porto
Period	10/3/09 → 10/5/09

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-04747-3_40

Cite this

Yamamoto, T., Bannai, H., Nagasaki, M., & Miyano, S. (2009). Better decomposition heuristics for the maximum-weight connected graph problem using betweenness centrality. In Discovery Science - 12th International Conference, DS 2009, Proceedings (pp. 465-472). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5808 LNAI). https://doi.org/10.1007/978-3-642-04747-3_40

Better decomposition heuristics for the maximum-weight connected graph problem using betweenness centrality. / Yamamoto, Takanori; Bannai, Hideo; Nagasaki, Masao et al.

Discovery Science - 12th International Conference, DS 2009, Proceedings. 2009. p. 465-472 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5808 LNAI).

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

Yamamoto, T, Bannai, H, Nagasaki, M & Miyano, S 2009, Better decomposition heuristics for the maximum-weight connected graph problem using betweenness centrality. in Discovery Science - 12th International Conference, DS 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5808 LNAI, pp. 465-472, 12th International Conference on Discovery Science, DS 2009, Porto, Portugal, 10/3/09. https://doi.org/10.1007/978-3-642-04747-3_40

Yamamoto T, Bannai H, Nagasaki M, Miyano S. Better decomposition heuristics for the maximum-weight connected graph problem using betweenness centrality. In Discovery Science - 12th International Conference, DS 2009, Proceedings. 2009. p. 465-472. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-04747-3_40

Yamamoto, Takanori ; Bannai, Hideo ; Nagasaki, Masao et al. / Better decomposition heuristics for the maximum-weight connected graph problem using betweenness centrality. Discovery Science - 12th International Conference, DS 2009, Proceedings. 2009. pp. 465-472 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{12046c14c7fa426783e6822d0cb5a35a,

title = "Better decomposition heuristics for the maximum-weight connected graph problem using betweenness centrality",

abstract = "We present new decomposition heuristics for finding the optimal solution for the maximum-weight connected graph problem, which is known to be NP-hard. Previous optimal algorithms for solving the problem decompose the input graph into subgraphs using heuristics based on node degree. We propose new heuristics based on betweenness centrality measures, and show through computational experiments that our new heuristics tend to reduce the number of subgraphs in the decomposition, and therefore could lead to the reduction in computational time for finding the optimal solution. The method is further applied to analysis of biological pathway data.",

author = "Takanori Yamamoto and Hideo Bannai and Masao Nagasaki and Satoru Miyano",

year = "2009",

doi = "10.1007/978-3-642-04747-3_40",

language = "English",

isbn = "3642047467",

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

pages = "465--472",

booktitle = "Discovery Science - 12th International Conference, DS 2009, Proceedings",

note = "12th International Conference on Discovery Science, DS 2009 ; Conference date: 03-10-2009 Through 05-10-2009",

}

TY - GEN

T1 - Better decomposition heuristics for the maximum-weight connected graph problem using betweenness centrality

AU - Yamamoto, Takanori

AU - Bannai, Hideo

AU - Nagasaki, Masao

AU - Miyano, Satoru

PY - 2009

Y1 - 2009

N2 - We present new decomposition heuristics for finding the optimal solution for the maximum-weight connected graph problem, which is known to be NP-hard. Previous optimal algorithms for solving the problem decompose the input graph into subgraphs using heuristics based on node degree. We propose new heuristics based on betweenness centrality measures, and show through computational experiments that our new heuristics tend to reduce the number of subgraphs in the decomposition, and therefore could lead to the reduction in computational time for finding the optimal solution. The method is further applied to analysis of biological pathway data.

AB - We present new decomposition heuristics for finding the optimal solution for the maximum-weight connected graph problem, which is known to be NP-hard. Previous optimal algorithms for solving the problem decompose the input graph into subgraphs using heuristics based on node degree. We propose new heuristics based on betweenness centrality measures, and show through computational experiments that our new heuristics tend to reduce the number of subgraphs in the decomposition, and therefore could lead to the reduction in computational time for finding the optimal solution. The method is further applied to analysis of biological pathway data.

UR - http://www.scopus.com/inward/record.url?scp=71049152085&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=71049152085&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-04747-3_40

DO - 10.1007/978-3-642-04747-3_40

M3 - Conference contribution

AN - SCOPUS:71049152085

SN - 3642047467

SN - 9783642047466

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

SP - 465

EP - 472

BT - Discovery Science - 12th International Conference, DS 2009, Proceedings

T2 - 12th International Conference on Discovery Science, DS 2009

Y2 - 3 October 2009 through 5 October 2009

ER -

Better decomposition heuristics for the maximum-weight connected graph problem using betweenness centrality

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this