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

Takanori Yamamoto, Hideo Bannai, Masao Nagasaki, Satoru Miyano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 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 languageEnglish
Title of host publicationDiscovery Science - 12th International Conference, DS 2009, Proceedings
Pages465-472
Number of pages8
Volume5808 LNAI
DOIs
Publication statusPublished - Nov 16 2009
Event12th International Conference on Discovery Science, DS 2009 - Porto, Portugal
Duration: Oct 3 2009Oct 5 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5808 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th International Conference on Discovery Science, DS 2009
CountryPortugal
CityPorto
Period10/3/0910/5/09

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All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Theoretical Computer Science

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 (Vol. 5808 LNAI, 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