Data-Driven Framework Development for Projection and Containment of Spreading Processes in Complex Social Networks

Project: Research project

Grant Program

Faculty Development Competitive Research Grants 2020-2022

Project Description

The proposed research project focuses on unraveling the dynamics of spreading process over large-scale social networks and devising efficient countermeasures to mitigate the spread. The core objectives are: (1) to model and assess individual-based spreading process dynamics over signed networks with positive and negative types of social interactions using models from epidemiology; (2) to extend the study to capture and characterize these dynamics over networks with multi-faceted social ties to account for both, the community structure and the nonlinear dependence of the infection pressure on the number of infected neighboring nodes in the network; and (3) to develop innovative immunization strategies to prevent the possibility of epidemic outbreaks under resource constraints. Justification of the entire mathematical analysis conducted in the project will be done using real-world data mined from social networks. The successful completion of the project leads to establishing more precise estimates of the infected fraction in the context of network science. The result of this project would profoundly improve our understanding of how social ties influence the propagation pattern of information flow in structured communities. The approaches proposed are novel and would pave the way for a rich set of research problems applicable to not only social networks, but also any general societal network witnessed in the modern world.
StatusActive
Effective start/end date1/1/2012/31/22

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Immunization
Epidemiology

Keywords

  • social network analysis
  • epidemic modeling
  • resource allocation
  • mean-field approximation
  • dynamical systems