Tracking Infection Diffusion in Social Networks: Filtering Algorithms and Threshold Bounds

TL;DR

This paper develops a Bayesian filtering approach to track infection or information spread in social networks modeled by SIS, using mean field approximation and polynomial dynamics, validated on Twitter data.

Contribution

It introduces a non-linear Bayesian filter for infection diffusion, linking network structure to diffusion thresholds, and validates it with real-world Twitter data.

Findings

The SIS model fits information diffusion well.

The non-linear filter accurately tracks diffusion dynamics.

Fundamental limits depend on network structure.

Abstract

This paper deals with the statistical signal pro- cessing over graphs for tracking infection diffusion in social networks. Infection (or Information) diffusion is modeled using the Susceptible-Infected-Susceptible (SIS) model. Mean field approximation is employed to approximate the discrete valued infected degree distribution evolution by a deterministic ordinary differential equation for obtaining a generative model for the infection diffusion. The infected degree distribution is shown to follow polynomial dynamics and is estimated using an exact non- linear Bayesian filter. We compute posterior Cramer-Rao bounds to obtain the fundamental limits of the filter which depend on the structure of the network. Considering the time-varying nature of the real world networks, the relationship between the diffusion thresholds and the degree distribution is investigated using generative models for real world networks. In addition, we validate the efficacy of our method with the diffusion data from a real-world online social system, Twitter. We find that SIS model is a good fit for the information diffusion and the non-linear filter effectively tracks the information diffusion.