Rock–paper–scissors dynamics from random walks on temporal multiplex networks

Gueuning, Martin; Cheng, Sibo; Lambiotte, Renaud; Delvenne, Jean‐Charles

doi:10.1093/comnet/cnz027

Cited by 5 publications

(4 citation statements)

References 29 publications

(35 reference statements)

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“…These methods are mostly based on random walks (Gueuning et al. 2019 ) or centrality measures in a network with the advantage of low computational cost. The use of graph theory in numerical simulation problems such as the Cuthill–McKee algorithm (Cuthill and McKee 1969 ) already occurs, for example, for sorting multidimensional grid points in a more efficient way (in terms of reducing the matrix band).…”

Section: State-observation Localization Based On Graph Clustering Methodsmentioning

confidence: 99%

A Graph Clustering Approach to Localization for Adaptive Covariance Tuning in Data Assimilation Based on State-Observation Mapping

et al. 2021

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An original graph clustering approach for the efficient localization of error covariances is proposed within an ensemble-variational data assimilation framework. Here, the localization term is very generic and refers to the idea of breaking up a global assimilation into subproblems. This unsupervised localization technique based on a linearized state-observation measure is general and does not rely on any prior information such as relevant spatial scales, empirical cutoff radii or homogeneity assumptions. Localization is performed via graph theory, a branch of mathematics emerging as a powerful approach to capturing complex and highly interconnected Earth and environmental systems in computational geosciences. The novel approach automatically segregates the state and observation variables in an optimal number of clusters, and it is more amenable to scalable data assimilation. The application of this method does not require underlying block-diagonal structures of prior covariance matrices. To address intercluster connectivity, two alternative data adaptations are proposed. Once the localization is completed, a covariance diagnosis and tuning are performed within each cluster, whose contribution is sequentially integrated into the entire covariance matrix. Numerical twin experiment tests show the reduced cost and added flexibility of this approach compared to global covariance tuning, and more accurate results yielded for both observation- and background-error parameter tuning.

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Section: State-observation Localization Based On Graph Clustering Methodsmentioning

confidence: 99%

A Graph Clustering Approach to Localization for Adaptive Covariance Tuning in Data Assimilation Based on State-Observation Mapping

et al. 2021

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“…5a) to simulate the virus spread in social networks. In general, multi-layer networks (De Domenico et al 2016) are widely used to study graph diffusion problems (Gueuning et al 2019). Recently, multilayer modelling has also been applied to COVID-19 spread simulation (Scabini et al 2021) where each layer refers to a potential contamination community, such as school, workplace or transport.…”

Section: Multi-layer Modelling Of Scale-free Networkmentioning

confidence: 99%

Real-time updating of dynamic social networks for COVID-19 vaccination strategies

Cheng

Pain

Guo

et al. 2023

J Ambient Intell Human Comput

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Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities.

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“…5(a)), for simulating the virus spread in social networks. In general, multi-layer (also known as "multiplex") networks [15] are widely used to study graph diffusion problems [24] and to define generalized versions of Pagerank [16]. Recently, multi-layer modelling has also been applied to COVID-19 spread simulation [49] where each layer refers to a potential contamination community, such as school, workplace or transport.…”

Section: Multi-layer Modelling Of Scale-free Networkmentioning

confidence: 99%

Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies

Cheng

Arcucci

Pain

et al. 2021

Preprint

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Vaccination strategy is crucial in fighting against the COVID-19 pandemic. Since the supply is limited, contact network-based interventions can be most powerful to set an optimal strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamical systems where the contact networks are highly time-variant. Furthermore, numerous mutations of SARS-CoV-2 impact considerably the current infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from the assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamical networks collected in a high school, following by sequential multi-layer networks, generated relying on the Barabasi-Albert model emulating the department of Computing at Imperial College London in the UK as an example.

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Rock–paper–scissors dynamics from random walks on temporal multiplex networks

Cited by 5 publications

References 29 publications

A Graph Clustering Approach to Localization for Adaptive Covariance Tuning in Data Assimilation Based on State-Observation Mapping

A Graph Clustering Approach to Localization for Adaptive Covariance Tuning in Data Assimilation Based on State-Observation Mapping

Real-time updating of dynamic social networks for COVID-19 vaccination strategies

Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies

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