Little Ball of Fur
Graph sampling extension library for NetworkX with Scikit-Learn like API.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Little Ball of Fur?
Little Ball of Fur is a graph sampling extension library built on top of NetworkX, offering a user-friendly interface similar to Scikit-Learn. It simplifies the process of working with large graphs by providing efficient sampling techniques.
Key differentiator
“Little Ball of Fur stands out by providing a Scikit-Learn like API for graph sampling, making it easier to integrate into existing machine learning workflows compared to other libraries.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
The library is built specifically on top of NetworkX and Python, limiting its use in other language ecosystems.
GitHub repository shows low activity with few contributors and infrequent updates to the codebase.
Sampling techniques, while efficient, may still face scalability issues when dealing with very large graph datasets due to memory constraints.
Fit analysis
Who is it for?
✓ Best for
Researchers working with large graph datasets who need efficient sampling techniques
Data scientists looking to apply machine learning on sampled graph data
Developers building applications that require handling of large network graphs
✕ Not a fit for
Projects requiring real-time graph processing and analysis
Applications where the GPL-3.0 license is not compatible with project requirements
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Next step
Get Started with Little Ball of Fur
Step-by-step setup guide with code examples and common gotchas.