NDlib: a Python Library to Model and Analyze Diffusion Processes Over Complex Networks
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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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Dec 2017.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 1; 2026: 2.
5 papers.
- 2021Transfer Learning for Node Regression Applied to Spreading Predictioncited by 1
- 2024Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimizationcited by 0
- 2025Fast Geometric Embedding for Node Influence Maximizationcited by 0
- 2026Social Simulations: from Agent-Based Modeling to Digital Twinscited by 0
- 2026Network World Models as Environments for Algorithm Design on Complex Systemscited by 0
- 2026Network World Models as Environments for Algorithm Design on Complex Systems1 Oct 2026
- 2026Social Simulations: from Agent-Based Modeling to Digital Twins15 Jul 2026
- 2025Fast Geometric Embedding for Node Influence Maximization9 Jun 2025
- 2024Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimization25 Mar 2024
- 2021Transfer Learning for Node Regression Applied to Spreading Prediction31 Mar 2021