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

arXiv: https://arxiv.org/abs/2404.17059 · HTML: https://mlsanity.com/paper/2404.17059/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 1 of 1)

- [A Community-Aware Framework for Social Influence Maximization](https://mlsanity.com/paper/2207.08937/) (2022)

## Built on by (most cited first, 4 of 4)

- [Bridging Visualization and Optimization: Multimodal Large Language Models on Graph-Structured Combinatorial Optimization](https://mlsanity.com/paper/2501.11968/) (2025)
- [Fractional Budget Allocation for Influence Maximization under General Marketing Strategies](https://mlsanity.com/paper/2407.05669/) (2024)
- [A Pressure-Based Diffusion Model for Influence Maximization on Social Networks](https://mlsanity.com/paper/2509.12822/) (2025)
- [A Community-Aware Framework for Influence Maximization with Explicit Accounting for Inter-Community Influence](https://mlsanity.com/paper/2512.23973/) (2025)

## Abstract (arXiv)

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.