CyNetDiff -- A Python Library for Accelerated Implementation of Network Diffusion Models

TL;DR

CyNetDiff is a Python library that accelerates network diffusion model simulations by integrating Cython components, enabling researchers to perform large-scale experiments more efficiently without sacrificing the ease of high-level programming.

Contribution

The paper introduces CyNetDiff, a Python library with Cython components that significantly improves the performance of network diffusion simulations.

Findings

Achieves faster simulation times for diffusion models

Provides an easy-to-use Python interface with high performance

Facilitates large-scale network experiments

Abstract

In recent years, there has been increasing interest in network diffusion models and related problems. The most popular of these are the independent cascade and linear threshold models. Much of the recent experimental work done on these models requires a large number of simulations conducted on large graphs, a computationally expensive task suited for low-level languages. However, many researchers prefer the use of higher-level languages (such as Python) for their flexibility and shorter development times. Moreover, in many research tasks, these simulations are the most computationally intensive task, so it would be desirable to have a library for these with an interface to a high-level language with the performance of a low-level language. To fill this niche, we introduce CyNetDiff, a Python library with components written in Cython to provide improved performance for these computationally intensive diffusion tasks.