StellarGraph is a Python library for machine learning on graph-structured (or equivalently, network-structured) data.

Graph-structured data represent entities, e.g., people, as nodes (or equivalently, vertices), and relationships between entities, e.g., friendship, as links (or equivalently, edges). Nodes and links may have associated attributes such as age, income, and time when a friendship was established, etc. StellarGraph supports analysis of both homogeneous networks (with nodes and links of one type) and heterogeneous networks (with more than one type of nodes and/or links).

The StellarGraph library implements several state-of-the-art algorithms for applying machine learning methods to discover patterns and answer questions using graph-structured data.

Keywords: graph machine learning, geometric deep learning, graph convolutional networks, graph neural networks, GCN, deep learning, StellarGraph, GraphSAGE, GAT

• Semi-Supervised Classification with Graph Convolutional Networks

• Inductive Representation Learning on Large Graphs

• Graph Attention Networks

• Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

• Deep Graph Infomax

• metapath2vec: Scalable Representation Learning for Heterogeneous Networks

• Modeling Relational Data with Graph Convolutional Networks

• Predict then Propagate: Graph Neural Networks meet Personalized PageRank

• Simplifying Graph Convolutional Networks

• node2vec: Scalable Feature Learning for Networks

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