Simulating Spreading of Multiple Interacting Processes in Complex Networks

TL;DR

This paper introduces Network Diffusion, a Python package designed to simulate and analyze the interaction of multiple spreading processes in complex networks, aiding in evaluating campaign effectiveness.

Contribution

The paper presents a new Python tool for simulating interacting spreading processes in complex networks, facilitating research and decision-making.

Findings

Network Diffusion enables simulation of multiple interacting processes.

The package helps evaluate campaign effectiveness in network scenarios.

Examples demonstrate practical applications of the tool.

Abstract

Investigating the interaction between spreading processes in complex networks is one of the most important challenges in network science. However, whether we would like to know how the information campaign will affect virus spreading or how the advertising campaign of the new iPhone will affect the sales of Samsung phones, we need an environment that will allow us to evaluate under what conditions our spreading campaign will be effective. Network Diffusion is a Python package that should help do that. In this paper, we introduce its operating principle and main functionalities, including simple examples of simulations that can be performed using it.