2021 Fiscal Year Annual Research Report
Simplifying social network data to predict spreading processes
| Project/Area Number |
20H04288
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| Research Institution | Kobe University |
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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) |
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)
Jusup Marko 東京工業大学, 科学技術創成研究院, 特任助教 (60762713)
村田 剛志 東京工業大学, 情報理工学院, 教授 (90242289)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Keywords | spreading processes / network theory / data structures |
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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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Research Products
(23 results)
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[Journal Article] Social Physics2021
Author(s)
Marko Jusup, Petter Holme, Kiyoshi Kanazawa, Misako Takayasu, Ivan Romic, Zhen Wang, Suncana Gecek, Tomislav Lipic, Boris Podobnik, Lin Wang, Wei Luo, Tin Klanjscek, Jingfang Fan, Stefano Boccaletti, Matjaz Perc
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Journal Title
Physics Reports
Volume: 948
Pages: 1-148
DOI
Peer Reviewed / Open Access / Int'l Joint Research
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[Journal Article] The physics of higher-order interactions in complex systems2021
Author(s)
Federico Battiston, Enrico Amico, Alain Barrat, Ginestra Bianconi, Guilherme Ferraz de Arruda, Benedetta Franceschiello, Iacopo Iacopini, Sonia K?fi, Vito Latora, Yamir Moreno, Micah M Murray, Tiago P Peixoto, Francesco Vaccarino, Giovanni Petri
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Journal Title
Nature Physics
Volume: 17
Pages: 1093-1098
DOI
Peer Reviewed / Open Access / Int'l Joint Research
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[Journal Article] Mobility in China, 2020: a tale of four phases2021
Author(s)
Suoyi Tan, Shengjie Lai, Fan Fang, Ziqiang Cao, Bin Sai, Bing Song, Bitao Dai, Shuhui Guo, Chuchu Liu, Mengsi Cai, Tong Wang, Mengning Wang, Jiaxu Li, Saran Chen, Shuo Qin, Jessica R Floyd, Zhidong Cao, Jing Tan, Xin Sun, Tao Zhou, Wei Zhang, Andrew J Tatem, Petter Holme, Xiaohong Chen, Xin Lu
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Journal Title
National Science Review
Volume: 8
Pages: nwab148
DOI
Peer Reviewed / Open Access / Int'l Joint Research
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