| 研究課題/領域番号 |
20H04288
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| 研究機関 | 神戸大学 |
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研究代表者 |
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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| 研究分担者 |
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)
上東 貴志 神戸大学, 計算社会科学研究センター, 教授 (30324908)
村田 剛志 東京工業大学, 情報理工学院, 教授 (90242289)
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| 研究期間 (年度) |
2020-04-01 – 2024-03-31
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| キーワード | spreading processes / network theory / data structures |
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| 研究実績の概要 |
In FY2022, we developed new methods and tools to deal with network data, concerning temporal networks, in two main research directions.
First, have discovered 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 hub nodes of the network to be connected simultaneously (thus extending the rich club coefficient for static networks). We studied data sets of different types, as well as models of temporal networks, and checked whether they exhibit such temporal-rich clubs. Second, we proposed a new representation of social ties built from temporal network data that considers the interdependency of social relationships. Using a series of temporal network models with tunable properties and tailored perturbations of these networks, we investigated the ability of this representation to discover state changes in social systems. Furthermore, we investigated the implications of this representation for modeling social contagion processes in a network.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
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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| 今後の研究の推進方策 |
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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