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

Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

From playlist Data driven modelling of complex systems

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Greg Pavliotis (DDMCS@Turing): Phase transitions for mean field limits of noisy interacting agents

Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

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 Software Engineering

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Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

From playlist Data driven modelling of complex systems

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From playlist Machine Learning

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Definition of a directed graphical model, or more precisely, what it means for a distribution to respect a directed acyclic graph.

From playlist Machine Learning

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Linear Regression

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From playlist Data-Driven Science and Engineering

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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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Stanford Seminar - Entrepreneurship in India: Its Current and Future Impact on Competitiveness

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From playlist Stanford EE380-Colloquium on Computer Systems - Seminar Series

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From playlist Large deviation theory in statistical physics: Recent advances and future challenges

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Thierry Goudon - A PDE model describing the immune cells-tumor growth interactions

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

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From playlist Edureka Live Classes 2020

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Second SIAM Activity Group on FME Virtual Talk

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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

Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

From playlist Data driven modelling of complex systems

Related pages

Degree distribution | Temporal network | Network science