Network Science Data-driven Modeling of Information Propagation in Multilayer and Multiplex Networks
Abstract
The study of dynamical processes on complex networks has been an active research area over the past decade. Our project focuses on two major and related classes of dynamical processes: i) Information propagation; and ii) Influence propagation. Unlike the majority of the current literature that focuses on single, isolated networks, we will consider these processes over multi-layer and multiplex networks that abound in nature and man-made infrastructures. The study of multi-layer networks will enable capturing the fact that information may propagate simultaneously over multiple social networks, while studying multiplex networks will make it possible to distinguish between different types of relationships that may exist in the network. Our goals include i) revealing relations between network structure and the threshold, probability, and size of information cascades; ii) developing algorithms to efficiently inhibit the propagation of information; iii) to understand how misinformation spreads in a population, and develop methods to counter this by injection of correct information. Our approach will be based primarily on data-driven studies of information and influence propagation. In particular, our project will explore real-world influence/information propagation processes through a collection of longitudinal data on Twitter-Instagram posts.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 16, 2018
- Source ID
- W911NF1710587
Entities
People
- Osman Yagan
Organizations
- Army Contracting Command
- Massachusetts Institute of Technology
- United States Army