2022 Fiscal Year Final Research Report
Community Detection Algorithm from Voting Process
| Project/Area Number |
21K21282
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Multi-year Fund |
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| Review Section |
1001:Information science, computer engineering, and related fields
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
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| Project Period (FY) |
2021-08-30 – 2023-03-31
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| Keywords | 確率過程 / ランダムグラフ / 平均時計算量 |
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| Outline of Final Research Achievements |
In this project, I studied the possibility of applying a stochastic process called the voting process on a graph to community detection from a theoretical point of view. Specifically, I investigated the behavior of a protocol called k-Majority on a random graph known as the stochastic block model, which is often used as a benchmark for community detection. The performance of the protocol varies depending on the value of parameter k, and I demonstrated that in certain situations, a larger value of k can improve the performance of community detection. Furthermore, I obtained improvements in computational lower bounds for the embedding clique problem, which is closely related to community detection.
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| Free Research Field |
理論計算機科学
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| Academic Significance and Societal Importance of the Research Achievements |
本研究はグラフ上の確率過程の解析とランダムグラフの計算複雑性という二つの成果を得た。前者の成果は、これまで完全グラフ上での振る舞いしかよく分かっていなかった合意モデルの振る舞いを、より複雑な確率的ブロックモデル上で解析することを可能にした。後者の成果はコミュニティ検出に関係のある埋め込みクリーク問題に対して、困難性の増幅と呼ばれる結果を示すことに成功した。この結果を得るにあたって新たに提案した枠組みはより幅広いクラスの問題に対して適用することが可能であり、理論計算機科学において非常に重要な意義を持つ。
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