Spread Model

Spread models aim to understand and predict the propagation of phenomena across networks, from information diffusion on social media to disease outbreaks and wildfire spread. Current research focuses on developing sophisticated models, such as Bayesian Mixture Hawkes processes and transmission neural networks, that incorporate diverse factors influencing spread dynamics, including source characteristics, content features, and network structure, while accounting for noisy and incomplete data. These advancements are crucial for improving early warning systems for public health crises, optimizing resource allocation in disaster management, and gaining insights into the mechanisms driving online virality and information cascades.