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.
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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.
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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
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Multiple variants of Graph Neural Networks (GCN, GAT, GraphSAGE, GraphWave, APPNP)
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Support for both homogeneous and heterogeneous graphs
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Probabilistic inference and model calibration
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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
