Information, ideas, and diseases, or more generally, contagions, spread over
space and time through individual transmissions via social networks, as well as
through external sources. A detailed picture of any diffusion process can be
achieved only when both a good network structure and individual diffusion
pathways are obtained. The advent of rich social, media and locational data
allows us to study and model this diffusion process in more detail than
previously possible. Nevertheless, how information, ideas or diseases are
propagated through the network as an overall process is difficult to trace.
This propagation is continuous over space and time, where individual
transmissions occur at different rates via complex, latent connections.
To tackle this challenge, a probabilistic spatiotemporal algorithm for
network diffusion (STAND) is developed based on the survival model in this
research. Both time and spatial distance are used as explanatory variables to
simulate the diffusion process over two different network structures. The aim
is to provide a more detailed measure of how different contagions are
transmitted through various networks where nodes are geographic places at a
large scale