| Project/Area Number |
20H04288
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 62020:Web informatics and service informatics-related
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| Research Institution | Kobe University (2021-2023)
Tokyo Institute of Technology (2020) |
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Principal Investigator |
Barrat Alain 神戸大学, 計算社会科学研究センター, リサーチフェロー (10867287)
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| Co-Investigator(Kenkyū-buntansha) |
上東 貴志 神戸大学, 計算社会科学研究センター, 教授 (30324908)
Holme Petter 神戸大学, 計算社会科学研究センター, リサーチフェロー (50802352)
村田 剛志 東京工業大学, 情報理工学院, 教授 (90242289)
Jusup Marko 東京工業大学, 科学技術創成研究院, 特任助教 (60762713)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥17,810,000 (Direct Cost: ¥13,700,000、Indirect Cost: ¥4,110,000)
Fiscal Year 2023: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2022: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2021: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2020: ¥5,720,000 (Direct Cost: ¥4,400,000、Indirect Cost: ¥1,320,000)
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| Keywords | spreading processes / complex networks / network science / covid-19 / theoretical epidemiology / graph data / social network data / network theory / data structures / Complex networks / temporal networks / epidemic processes / social contagion |
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| Outline of Research at the Start |
We aim at finding new ways to extract relevant structures from complex data, to represent these data for integration in data-driven contagion models, and to use these new tools in predictive modeling for epidemic spreading.
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| Outline of Annual Research Achievements |
In FY 2023, we primarily investigated the relevance of network structures extracted from data in spreading dynamics. We focused on how these structures could be used to design efficient containment strategies. The project examined the role of backbones and temporal cores in spreading processes and found that acting on these structures can help contain epidemic outbreaks. Our project also explored the relative timescales of observation, network evolution, and the dynamic process under study. By identifying the optimal time window for aggregating time-varying networks, we were able to reduce redundancy and simplify the modeling of spreading processes on temporal networks by embedding techniques. We, furthermore, investigated the impact of incompleteness or noise on the outcome of data-driven models using previously developed representations and developed approaches to compensate for resulting biases.
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| Research Progress Status |
令和5年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和5年度が最終年度であるため、記入しない。
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