| Session | |
WS-T53: Many metrics and models for network diffusion and learning
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| Session Abstract | |
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From infectious diseases to innovations, from policies to norms, networks often influence how outcomes are distributed. This workshop introduces the many analytic and visualisation tools available in the ‘manynet’, ‘migraph’ and associated packages in R for studying network diffusion, contagion, or learning. First, we look at the tools available for simulating various contagion processes, including simple and complex diffusion. We extend these models to a range of different compartment models, e.g. SEIRS, that can better represent more varied processes, and suggest how to evaluate the fit of these simulations with observed diffusions. Second, we look at tools for measuring, describing, or inferring aspects of these processes, from hazard rates to thresholds. We show how they can be used on observed diffusions too, so please bring data from any salient projects you are working on. Lastly, we will explore how, with the rest of the tools available in ‘manynet’ and ‘migraph’, we can identify points of intervention to accelerate or obstruct diffusion. Because these procedures are based on ‘manynet’, they work with many different network formats and types, including ‘igraph’ and ‘network’ classes, as well as directed, multimodal, signed and multiplex data. Familiarity with R and RStudio is recommended. | |
| Presentations | |
Many metrics and models for network diffusion and learning From infectious diseases to innovations, from policies to norms, networks often influence how outcomes are distributed. This workshop introduces the many analytic and visualisation tools available in the ‘manynet’, ‘migraph’ and associated packages in R for studying network diffusion, contagion, or learning. First, we look at the tools available for simulating various contagion processes, including simple and complex diffusion. We extend these models to a range of different compartment models, e.g. SEIRS, that can better represent more varied processes, and suggest how to evaluate the fit of these simulations with observed diffusions. Second, we look at tools for measuring, describing, or inferring aspects of these processes, from hazard rates to thresholds. We show how they can be used on observed diffusions too, so please bring data from any salient projects you are working on. Lastly, we will explore how, with the rest of the tools available in ‘manynet’ and ‘migraph’, we can identify points of intervention to accelerate or obstruct diffusion. Because these procedures are based on ‘manynet’, they work with many different network formats and types, including ‘igraph’ and ‘network’ classes, as well as directed, multimodal, signed and multiplex data. Familiarity with R and RStudio is recommended. | |