Random graphs

Activity-driven model

In network science, the activity-driven model is a temporal network model in which each node has a randomly-assigned "activity potential", which governs how it links to other nodes over time. Each node (out of total) has its activity potential drawn from a given distribution . A sequence of timesteps unfolds, and in each timestep each node forms ties to random other nodes at rate (more precisely, it does so with probability per timestep). All links are then deleted after each timestep. Properties of time-aggregated network snapshots are able to be studied in terms of . For example, since each node after timesteps will have on average outgoing links, the degree distribution after timesteps in the time-aggregated network will be related to the activity-potential distribution by Spreading behavior according to the SIS epidemic model was investigated on activity-driven networks, and the following condition was derived for large-scale outbreaks to be possible: where is the per-contact transmission probability, is the per-timestep recovery probability, and are the first and second moments of the random activity-rate . (Wikipedia).

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Tony Lelievre (DDMCS@Turing): Coarse-graining stochastic dynamics

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From playlist Data driven modelling of complex systems

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From playlist Data driven modelling of complex systems

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From playlist Data-Driven Control with Machine Learning

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From playlist Machine Learning

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From playlist Machine Learning

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From playlist Statistical Physics of Complex Systems - 2022

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From playlist Stochastic Thermodynamics, Active Matter and Driven Systems - 2017

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From playlist Stochastic Thermodynamics, Active Matter and Driven Systems - 2017

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From playlist Workshop "Tissue growth and movement" - 10-14 January 2022

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From playlist SIAM Activity Group on FME Virtual Talk Series

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Jonathan Weare (DDMCS@Turing): Stratification for Markov Chain Monte Carlo

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From playlist Data driven modelling of complex systems

Related pages

Degree distribution | Temporal network | Network science