| Project/Area Number |
18K18125
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 61040:Soft computing-related
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| Research Institution | Saitama University |
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Principal Investigator |
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 複雑ネットワーク / 非線形時系列解析 / テンポラルネットワーク / ネットワーク間距離 / グラフ間距離 / 人間行動データ / テンポラル・ネットワーク / 複雑系 / データ解析 / 時系列解析 |
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| Outline of Final Research Achievements |
Spatio-temporal changes in the relationship between any two individuals in a population can be described by temporal networks, which is a series of graphs consisting of a set of nodes and links. In this study, incorporating a notion of inter-netowork distance, we proposed a new method for analyzing temporal networks and demonstrated its effectiveness in the prediction of temporal networks and structural analyses of various types of networks. We also investigated information diffusion on temporal networks and discuss the applicability of our method to its prediction and control.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究ではグラフ間距離のテンポラルネットワーク解析に対する適用可能性の調査，有効性の検証に取り組んだ．テンポラルネットワークは大規模化・多様化するデータを効率的に解析するための重要なツールの一つであり，本研究はその発展に寄与するという学術的意義を有する．
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