CyNetDiff -- A Python Library for Accelerated Implementation of Network Diffusion Models
This work addresses a bottleneck for researchers in network science who need efficient simulations in Python, though it is incremental as it builds on existing models with a new implementation.
The authors tackled the computational expense of simulating network diffusion models like independent cascade and linear threshold on large graphs by introducing CyNetDiff, a Python library with Cython components that accelerates these tasks, providing performance comparable to low-level languages while maintaining high-level language flexibility.
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.