Simplifying social network data to predict spreading processes

2023 Fiscal Year Final Research Report

• Project/Area Number: 20H04288
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Single-year Grants
• Section: 一般
• Review Section: Basic Section 62020:Web informatics and service informatics-related
• Research Institution: Kobe University (2021-2023); Tokyo Institute of Technology (2020)
• 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)
• 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)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: Network epidemiology / Digital epidemiology / Network theory / Data science / Graph data

Outline of Final Research Achievements

New technologies recording aspects of our life are very valuable both for science and commercial interests. Within this program, we have developed new methods of handling such data-both how to represent it as data structures to facilitate the discovery of the drivers of spreading processes (like disease epidemics) and how to generate structurally similar, synthetic data. We have, furthermore, advanced the theory of spreading processes, including new data from the Covid-19 pandemics that happened during the program.

Free Research Field

Complex networks

Academic Significance and Societal Importance of the Research Achievements

このプログラムにより、病気や情報の拡散に関する理解が深まった。具体的には、伝染病の緩和に役立つように、人間の移動と接触データを伝染病モデルに統合する方法が進歩した。