StellarGraph
Machine Learning on Graphs for network-structured data.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is StellarGraph?
StellarGraph is a Python library that enables machine learning on graph-structured data, providing tools and algorithms to analyze complex networks and relationships within the data.
Key differentiator
“StellarGraph stands out by providing comprehensive support for graph neural networks and deep learning on graphs, making it ideal for complex network analysis tasks.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Examples and tutorials are primarily focused on basic usage, lacking detailed explanations for complex scenarios
Scalability tests show significant slowdowns when processing graphs with millions of nodes and edges
Fit analysis
Who is it for?
✓ Best for
Researchers and practitioners working with graph-structured data who need advanced machine learning capabilities.
Projects requiring integration of TensorFlow or Keras for deep learning on graphs.
✕ Not a fit for
Developers looking for a cloud-based service without the need to manage local installations.
Teams that require real-time processing and cannot afford the latency associated with local computation.
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
Next step
Get Started with StellarGraph
Step-by-step setup guide with code examples and common gotchas.