# NDlib: a Python Library to Model and Analyze Diffusion Processes Over Complex Networks

arXiv: https://arxiv.org/abs/1801.05854 · HTML: https://mlsanity.com/paper/1801.05854/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2017.

## Built on by (most cited first, 5 of 5)

- [Transfer Learning for Node Regression Applied to Spreading Prediction](https://mlsanity.com/paper/2104.00088/) (2021)
- [Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimization](https://mlsanity.com/paper/2403.16818/) (2024)
- [Fast Geometric Embedding for Node Influence Maximization](https://mlsanity.com/paper/2506.07435/) (2025)
- [Social Simulations: from Agent-Based Modeling to Digital Twins](https://mlsanity.com/paper/2607.13693/) (2026)
- [Network World Models as Environments for Algorithm Design on Complex Systems](https://mlsanity.com/paper/2610.01048/) (2026)

## Abstract (arXiv)

Nowadays the analysis of dynamics of and on networks represents a hot topic in the Social Network Analysis playground. To support students, teachers, developers and researchers in this work we introduce a novel framework, namely NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians.