STAND: A spatio-temporal algorithm for network diffusion simulation

Fangcao Xu, Bruce Desmarais, Donna Jean Peuquet

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Information, ideas, and diseases, or more generally, contagions, spread over time and space 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 detailed network structure and individual diffusion pathways are obtained. Studying such diffusion networks provides valuable insights to understand important actors in carrying and spreading contagions and to help predict occurrences of new infections. Most prior research focuses on modeling diffusion process only in the temporal dimension. The advent of rich social, media and geo-tagged data now allows us to study and model this diffusion process in both temporal and spatial dimensions than previously possible. Nevertheless, how information, ideas or diseases are propagated through the network as an overall spatiotemporal process is difficult to trace. This propagation is continuous over time and space, where individual transmissions occur at different rates via complex and latent connections. To tackle this challenge, a probabilistic spatiotemporal algorithm for network diffusion simulation (STAND) is developed based on the survival model in this research. Both time and geographic 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 denote geographic locations at a large scale.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2020
EditorsTaylor Anderson, Joon-Seok Kim, Ashwin Shashidharan
PublisherAssociation for Computing Machinery, Inc
Pages20-29
Number of pages10
ISBN (Electronic)9781450381611
DOIs
StatePublished - Nov 3 2020
Event3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2020 - Seattle, United States
Duration: Nov 3 2020 → …

Publication series

NameProceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2020

Conference

Conference3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2020
CountryUnited States
CitySeattle
Period11/3/20 → …

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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