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Feb. 19, 2019
Apr. 13, 2018

StellarGraph Machine Learning library logo

Stellar Graph Machine Learning Library


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.

The StellarGraph library can be used to solve tasks using graph-structured data, such as:

  • Representation learning for nodes and edges, to be used for visualisation and various downstream machine learning tasks;
  • Classification and attribute inference of nodes or edges;
  • Link prediction.

We provide examples of using StellarGraph to solve such tasks using several real-world datasets.

Guiding Principles

StellarGraph uses the Keras library and adheres to the same guiding principles as Keras: user-friendliness, modularity, and easy extendability. Modules and layers of StellarGraph library are designed so that they can be used together with standard Keras layers and modules, if required. This enables flexibility in using existing or creating new models and workflows for machine learning on graphs.

Getting Started

To get started with StellarGraph you'll need data structured as a homogeneous or heterogeneous graph, including attributes for the entities represented as graph nodes. NetworkX is used to represent the graph and Pandas or Numpy are used to store node attributes.

Detailed and narrated examples of various machine learning workflows on network data, supported by StellarGraph, from data ingestion into graph structure to inference, are given in the demos directory of this repository.


StellarGraph is a Python 3 library and requires Python version 3.6 to function (note that the library uses Keras with the Tensorflow backend, and thus does not currently work in python 3.7). The required Python version can be downloaded and installed from Alternatively, use the Anaconda Python environment, available from

The StellarGraph library can be installed in one of two ways, described next.

Install StellarGraph using pip:

To install StellarGraph library from PyPi using pip, execute the following command:

pip install stellargraph

Some of the examples require installing additional dependencies as well as stellargraph. To install these dependencies using pip, execute the following command:

pip install stellargraph[demos]

Install StellarGraph from Github source:

First, clone the StellarGraph repository using git:

git clone

Then, cd to the StellarGraph folder, and install the library by executing the following commands:

cd stellargraph
pip install -r requirements.txt
pip install .

Running the examples

See the README in the demos directory for more information about the examples and how to run them.


The StellarGraph library currently includes the following algorithms for graph machine learning:

  • GraphSAGE [1]

    • Supports representation learning, node classification/regression, and link prediction for homogeneous networks. The current implementation supports multiple aggregation methods, including mean, maxpool, meanpool, and attentional aggregators.
  • HinSAGE

    • Extension of GraphSAGE algorithm to heterogeneous networks. Supports representation learning, node classification/regression, and link prediction/regression for heterogeneous graphs. The current implementation supports mean aggregation of neighbour nodes, taking into account their types and the types of links between them.
  • Node2Vec [2]

    • Unsupervised representation learning for homogeneous networks, taking into account network structure while ignoring node attributes. The node2vec algorithm is implemented by combining StellarGraph's random walk generator with the word2vec algorithm from Gensim. Learned node representations can be used in downstream machine learning models implemented using Scikit-learn, Keras, Tensorflow or any other Python machine learning library.
  • Metapath2Vec [3]

    • Unsupervised, metapath-guided representation learning for heterogeneous networks, taking into account network structure while ignoring node attributes. The implementation combines StellarGraph's metapath-guided random walk generator and Gensim word2vec algorithm. As with node2vec, the learned node representations (node embeddings) can be used in downstream machine learning models to solve tasks such as node classification, link prediction, etc, for heterogeneous networks.
  • Graph Convolutional Networks (GCN) [4]

    • An implementation supports representation learning, node classification for homogeneous graphs.

Getting Help

Documentation for StellarGraph can be found here.


buildkite integration

Pipeline is defined in .buildkite/pipeline.yml

Docker images

  • Tests: Uses the official python:3.6 image.
  • Style: Uses black from the stellargraph docker hub organisation.


  1. Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec arXiv:1706.02216 [cs.SI], 2017. (link)

  2. Node2Vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016. (link)

  3. Metapath2Vec: Scalable Representation Learning for Heterogeneous Networks. Yuxiao Dong, Nitesh V. Chawla, and Ananthram Swami. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 135–144, 2017 (link)

  4. Graph Convolutional Networks (GCN): Semi-Supervised Classification with Graph Convolutional Networks. Thomas N. Kipf, Max Welling. International Conference on Learning Representations (ICLR), 2017 (link)