| 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 |
Granted (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 / 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 FY2021, we will develop new methods and tools to deal with network data, in particular concerning temporal networks, in two main research directions.
On the one hand, we will define and investigate new types of relevant structures in temporal network data, such as the 'temporal rich club': we will define a quantity that describes the tendency of well connected nodes of the network to be connected together in a simultaneous fashion (generalizing hence the usual rich club coefficient for static networks, which does not take into account temporality). We will study data sets of different types, as well as models of temporal networks, and check whether they exhibit such temporal rich clubs.
On the other hand, we will consider a new representation of social ties built from temporal network data that takes into account the interdependency of social relationships. Using a series of temporal network models with tunable properties, and tailored perturbations of these networks, we will investigate the ability of this representation to detect perturbations in a social system. We will moreover use this representation to propose new ways of modeling social contagion processes in a network.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
At present, we are in phase with the proposed program. We are currently investigating methods to simplify and compress data streams from proximity data (such as commonly used in, e.g., Covid-19 modeling). We have finished most of the proposed project and are currently preparing to extend the methods to higher-order network representations beyond regular binary networks.
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| Strategy for Future Research Activity |
In the future, we will validate the relevance of the structures extracted from data in shaping spreading processes and explore how these structures can be used to develop efficient containment strategies. The project will examine the role of backbones and central temporal cores in spreading processes and determine whether acting on these structures can help to contain the spread of epidemics. The project will also explore the relative timescales of observation, network evolution, and the dynamic process under study. By identifying the optimal time window to aggregate the time-varying network, the project will be able to reduce redundancy and simplify the modeling of spreading processes on temporal networks using embedding techniques. The project will also investigate the impact of incompleteness or noise on the outcome of data-driven models using previously developed representations and propose approaches to compensate for resulting biases. Finally, we will investigate these questions for higher-order network representations.
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