Suppressed epidemics in multi-relational networks

TL;DR

This paper introduces a two-state epidemic model on multi-relational networks, revealing complex suppression phenomena and phase transitions influenced by link weights and probabilities, supported by mean field and local environment theories.

Contribution

It presents a novel epidemic model on multi-relational networks with analytical and simulation insights into suppression effects and phase behavior.

Findings

Non-monotonic behavior of infection fraction with link probability p

Identification of an optimal suppression point at small to moderate weight ratios

Agreement between theory and simulations highlighting the importance of spatial correlations

Abstract

A two-state epidemic model in networks with links mimicking two kinds of relationships between connected nodes is introduced. Links of weights w1 and w0 occur with probabilities p and 1-p, respectively. The fraction of infected nodes rho(p) shows a non-monotonic behavior, with rho drops with p for small p and increases for large p. For small to moderate w1/w0 ratios, rho(p) exhibits a minimum that signifies an optimal suppression. For large w1/w0 ratios, the suppression leads to an absorbing phase consisting only of healthy nodes within a range p_L =< p =< p_R, and an active phase with mixed infected and healthy nodes for p < p_L and p>p_R. A mean field theory that ignores spatial correlation is shown to give qualitative agreement and capture all the key features. A physical picture that emphasizes the intricate interplay between infections via w0 links and within clusters formed by nodes carrying the w1 links is presented. The absorbing state at large w1/w0 ratios results when the clusters are big enough to disrupt the spread via w0 links and yet small enough to avoid an epidemic within the clusters. A theory that uses the possible local environments of a node as variables is formulated. The theory gives results in good agreement with simulation results, thereby showing the necessity of including longer spatial correlations.