STELLARGRAPH

Library

Graph machine learning at scale

for better informed decisions

StellarGraph is a commercial grade, open source

graph machine learning library written in Python for

data scientists, analysts and data engineers.

Many real-world datasets can be naturally represented as graphs, with nodes representing entities and links representing

relationships or interactions between entities.

In predictive tasks, this graph representation of data

can help improve the predictive power if the underlying graph structure can be fully utilised by machine learning algorithms.

But bridging the gap between graph analytics tools and

machine learning frameworks can be challenging.

 

We've developed an open-source python library to democratise machine learning on graphs for data scientists, analysts and data engineers wanting to experiment with graph machine learning techniques, and/or apply them to their graph-structured data.

ALGORITHMS / CAPABILITIES

  • Multiple variants of Graph Neural Networks (GCN, GAT, GraphSAGE, GraphWave, APPNP)

  • Support for both homogeneous and heterogeneous graphs

  • Probabilistic inference and model calibration

  • Comprehensive demos including node classification, link prediction, unsupervised representation learning / graph embeddings, and interpretability.

TECHNOLOGY

Python  |  TensorFlow  |  Keras  |  NetworkX  |  scikit-learn

OPEN SOURCE

Free to use

(Apache 2.0 license)

and maintained by the StellarGraph team

EASY TO USE

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

CUTTING EDGE

Research backed, engineering-proven algorithms

Start your graph machine learning journey!