Reveal hidden insights from your data with machine learning on graphs

StellarGraph is a commercial grade, open source, graph machine learning library written for Data Scientists in Python.

Many real-world datasets can be naturally represented as networks/graphs, with nodes representing entities, and links representing relations or interactions between entities.


In many predictive tasks, this network representation of data can help improve the predictive power, if the underlying network structure can be fully utilised by machine learning algorithms. However, bridging the gap between graph analytics tools and machine learning frameworks may be challenging.


To address these challenges, we've developed an open-source python library to democratise machine learning on networks/graphs for data scientists, developers, and researchers wanting to experiment with graph machine learning techniques and/or apply them to their network-structured data.

  • Open-source, actively developed and maintained

  • Easy to use, modular and extendable

  • State-of-art algorithms and capabilities:

  • Multiple variants of Graph Neural Networks (GCN, GAT, GraphSAGE, etc.)

  • Support for both homogeneous and heterogeneous graphs

  • Probabilistic inference and model calibration

  • Comprehensive set of demos

  • Technology: python, tensorflow, keras, networkx, scikit-learn.

Start Your Graph Machine Learning Journey!


The library is completely open source, Apache 2.0, and maintained by the StellarGraph team


Modular and extendable Python code, with a comprehensive set of demos


Research backed, engineering proven algorithms


Want to deepen your insights with comparison tools and scale your models to production scale data? Get in touch to register your interest