Information Propagation in Clustered Multilayer Networks

TL;DR

This paper models information spread in multi-layered clustered networks, revealing how clustering and transmissibility influence epidemic thresholds and final reach, with implications for online and physical social interactions.

Contribution

It introduces a multi-layer clustered network model for information diffusion and analyzes how clustering affects epidemic thresholds and spread dynamics.

Findings

Higher clustering increases epidemic threshold and reduces epidemic size.

Low transmissibility info spreads better in small, dense networks.

High transmissibility info spreads more effectively in large, loose networks.

Abstract

In today's world, individuals interact with each other in more complicated patterns than ever. Some individuals engage through online social networks (e.g., Facebook, Twitter), while some communicate only through conventional ways (e.g., face-to-face). Therefore, understanding the dynamics of information propagation among humans calls for a multi-layer network model where an online social network is conjoined with a physical network. In this work, we initiate a study of information diffusion in a clustered multi-layer network model, where all constituent layers are random networks with high clustering. We assume that information propagates according to the SIR model and with different information transmissibility across the networks. We give results for the conditions, probability, and size of information epidemics, i.e., cases where information starts from a single individual and reaches a positive fraction of the population. We show that increasing the level of clustering in either one of the layers increases the epidemic threshold and decreases the final epidemic size in the whole system. An interesting finding is that information with low transmissibility spreads more effectively with a small but densely connected social network, whereas highly transmissible information spreads better with the help of a large but loosely connected social network.