# STAND: A Spatio-Temporal Algorithm for Network Diffusion Simulation

## TL;DR

STAND is a probabilistic spatiotemporal algorithm that models the diffusion of contagions over space and time using survival analysis, leveraging rich data to better understand transmission pathways in networks.

## Contribution

The paper introduces a novel spatiotemporal diffusion algorithm, STAND, that incorporates geographic and temporal data to simulate contagion spread more accurately.

## Key findings

- Effective simulation of diffusion over different network structures
- Utilizes spatial distance and time as explanatory variables
- Provides detailed insights into contagion transmission pathways

## Abstract

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.