Designing Algorithms for Network Analysis with Combinatorial Optimization Theory
| Project/Area Number |
17K00028
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Mathematical informatics
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| Research Institution | Keio University |
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Principal Investigator |
Kakimura Naonori 慶應義塾大学, 理工学部(矢上), 准教授 (30508180)
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| Project Period (FY) |
2017-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | 組合せ最適化 / アルゴリズム / 近似アルゴリズム / ストリーミング計算 / 離散構造 / 劣モジュラ関数 / ストリーミングアルゴリズム / マトロイド / マッチング / コミュニティ検出 / 数理工学 / 情報基礎 / 数理モデル |
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| Outline of Final Research Achievements |
In this project, we proposed combinatorial optimization models for large-scale network analysis, and designed efficient algorithms with theoretical guarantees for the proposed models. In particular, we investigated the computational complexity and approximability of streaming algorithms for the problem of maximizing submodular functions with some constraints, and proposed new optimization models for the community detection problem in networks. These problems are general optimization problems that have been studied widely in theory and practice. Our results have been presented in refereed conferences in theoretical computer science and data mining, and published in international refereed journals.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究課題では，組合せ最適化手法を用いて，ネットワークを解析するための汎用的なモデルと効率的な計算手法を確立することを目指した．研究成果は，ネットワーク解析という実用に現れる問題に対して，組合せ最適化という理論的な手法を用いてアプローチするものであり，理論と実用の両面からの有用性が期待される．近年，機械学習や人工知能など情報科学分野ではアルゴリズムの理論的な性質を保証することが重要となっており，組合せ最適化理論を用いた本研究のアプローチは国際的な研究動向に沿った研究成果と言える．また，フランス，アメリカ，ハンガリーなどの研究者と連携し研究を行なうことで，国際的な研究ネットワークを構築した．
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Report
(7 results)
Research Products
(55 results)
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[Journal Article] Reforming an Envy-Free Matching2022
Author(s)
Takehiro Ito, Yuni Iwamasa, Naonori Kakimura, Naoyuki Kamiyama, Yusuke Kobayashi, Yuta Nozaki, Yoshio Okamoto and Kenta Ozeki
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Journal Title
The 36th AAAI Conference on Artificial Intelligence (AAAI)
Volume: --
Related Report
Peer Reviewed
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[Journal Article] Spectral Aspects of Symmetric Matrix Signings2019
Author(s)
Charlie Carlson, Karthekeyan Chandrasekaran, Hsien-Chih Chang, Naonori Kakimura and Alexandra Kolla
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Journal Title
The 44th International Symposium on Mathematical Foundations of Computer Science (MFCS 2019), LIPIcs
Volume: 81
Pages: 1-13
Related Report
Peer Reviewed / Open Access / Int'l Joint Research
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[Journal Article] Reconfiguration of Maximum-Weight b-Matchings in a Graph2017
Author(s)
Takehiro Ito, Naonori Kakimura, Naoyuki Kamiyama, Yusuke Kobayashi, and Yoshio Okamoto
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Journal Title
Proceedings of 23rd Annual International Computing and Combinatorics Conference (COCOON 2017)
Volume: ---
Pages: 287-296
DOI
NAID
ISBN
9783319623887, 9783319623894
Related Report
Peer Reviewed / Open Access
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