2021 Fiscal Year Final Research Report
Analysis on high-resolution spatiotemporal patterns by a graph distance and its application to information diffusion analysis
| 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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| 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 |
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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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| Free Research Field |
複雑ネットワーク解析，非線形時系列解析
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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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