| Project/Area Number |
18K11434
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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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| Review Section |
Basic Section 61030:Intelligent informatics-related
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | machine learning / Graph analysis / bioinformatics / large graph / graph neural networks / Convex clustering / graph Laplacian / hypergraph / sparsistency / Learning on graphs / distances on graphs / semi-supervised learning / graph embedding |
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| Outline of Final Research Achievements |
We have achieved some theoretical and application results on this project. For theoretical, we laid a foundation for learning on hypergraphs, an extension of graphs. We also could apply learning on graphs to complicated applications involving molecules and its interactions with others by leveraging graph information.
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| Academic Significance and Societal Importance of the Research Achievements |
Our achievements help pay ways for further research into more complicated problems in the area of graphs, hypergraphs and application on molecular learning. This may contribute to further research and development in biomedical applications.
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