StellarGraph is a commercial grade, open source
graph machine learning library written in Python for
data scientists, analysts and data engineers.
Many realworld 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 opensource 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 graphstructured 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  scikitlearn
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, engineeringproven algorithms