Modeling diffusion in networks with communities: a multitype branching process approach

TL;DR

This paper introduces a multitype branching process framework to analyze diffusion in networks with community structures, enabling the calculation of key propagation metrics using limited network information.

Contribution

It develops a theoretical approach for modeling diffusion in community-structured networks using multi-type branching processes, applicable with minimal network data.

Findings

Accurately predicts cascade sizes and extinction probabilities.

Demonstrates the impact of initial seeding location on diffusion outcomes.

Validates the framework on stochastic block and log-normal networks.

Abstract

The dynamics of diffusion in complex networks are widely studied to understand how entities, such as information, diseases, or behaviors, spread in an interconnected environment. Complex networks often present community structure, and tools to analyze diffusion processes on networks with communities are needed. In this paper, we develop theoretical tools using multi-type branching processes to model and analyze diffusion processes, following a simple contagion mechanism, across a broad class of networks with community structure. We show how, by using limited information about the network -- the degree distribution within and between communities -- we can calculate standard statistical characteristics of propagation dynamics, such as the extinction probability, hazard function, and cascade size distribution. These properties can be estimated not only for the entire network but also for each community separately. Furthermore, we estimate the probability of spread crossing from one community to another where it is not currently spreading. We demonstrate the accuracy of our framework by applying it to two specific examples: the Stochastic Block Model and a log-normal network with community structure. We show how the initial seeding location affects the observed cascade size distribution on a heavy-tailed network and that our framework accurately captures this effect.