StellarGraph is a Python library for machine learning on graphs and networks.

• Introduction
• Getting Started
• Getting Help
• Example: GCN
• Algorithms
• Installation
  • Install StellarGraph using PyPI
  • Install StellarGraph in Anaconda Python
  • Install StellarGraph from GitHub source
• Citing
• References

The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. It can solve many machine learning tasks:

• 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;
• Classification of whole graphs;
• Link prediction;
• Interpretation of node classification [8].

Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. For example, a graph can contain people as nodes and friendships between them as links, with data like a person's age and the date a friendship was established. StellarGraph supports analysis of many kinds of graphs:

• homogeneous (with nodes and links of one type),
• heterogeneous (with more than one type of nodes and/or links)
• knowledge graphs (extreme heterogeneous graphs with thousands of types of edges)
• graphs with or without data associated with nodes
• graphs with edge weights

StellarGraph is built on TensorFlow 2 and its Keras high-level API, as well as Pandas and NumPy. It is thus user-friendly, modular and extensible. It interoperates smoothly with code that builds on these, such as the standard Keras layers and scikit-learn, so it is easy to augment the core graph machine learning algorithms provided by StellarGraph. It is thus also easy to install with pip or Anaconda.

The numerous detailed and narrated examples are a good way to get started with StellarGraph. There is likely to be one that is similar to your data or your problem (if not, let us know).

You can start working with the examples immediately in Google Colab or Binder by clicking the and badges within each Jupyter notebook.

Alternatively, you can run download a local copy of the demos and run them using jupyter. The demos can be downloaded by cloning the master branch of this repository, or by using the curl command below:

curl -L https://github.com/stellargraph/stellargraph/archive/master.zip | tar -xz --strip=1 stellargraph-master/demos

The dependencies required to run most of our demo notebooks locally can be installed using one of the following:

• Using pip: pip install stellargraph[demos]
• Using conda: conda install -c stellargraph stellargraph

(See Installation section for more details and more options.)

If you get stuck or have a problem, there are many ways to make progress and get help or support:

• Read the documentation
• Consult the examples
• Contact us:
  • Ask questions and discuss problems on the StellarGraph Discussions forum
  • File an issue
  • Send us an email at [email protected]

One of the earliest deep machine learning algorithms for graphs is a Graph Convolution Network (GCN) [6]. The following example uses it for node classification: predicting the class from which a node comes. It shows how easy it is to apply using StellarGraph, and shows how StellarGraph integrates smoothly with Pandas and TensorFlow and libraries built on them.

Data for StellarGraph can be prepared using common libraries like Pandas and scikit-learn.

import pandas as pd
from sklearn import model_selection

def load_my_data():
    # your own code to load data into Pandas DataFrames, e.g. from CSV files or a database
    ...

nodes, edges, targets = load_my_data()

# Use scikit-learn to compute training and test sets
train_targets, test_targets = model_selection.train_test_split(targets, train_size=0.5)

This is the only part that is specific to StellarGraph. The machine learning model consists of some graph convolution layers followed by a layer to compute the actual predictions as a TensorFlow tensor. StellarGraph makes it easy to construct all of these layers via the GCN model class. It also makes it easy to get input data in the right format via the StellarGraph graph data type and a data generator.

import stellargraph as sg
import tensorflow as tf

# convert the raw data into StellarGraph's graph format for faster operations
graph = sg.StellarGraph(nodes, edges)

generator = sg.mapper.FullBatchNodeGenerator(graph, method="gcn")

# two layers of GCN, each with hidden dimension 16
gcn = sg.layer.GCN(layer_sizes=[16, 16], generator=generator)
x_inp, x_out = gcn.in_out_tensors() # create the input and output TensorFlow tensors

# use TensorFlow Keras to add a layer to compute the (one-hot) predictions
predictions = tf.keras.layers.Dense(units=len(ground_truth_targets.columns), activation="softmax")(x_out)

# use the input and output tensors to create a TensorFlow Keras model
model = tf.keras.Model(inputs=x_inp, outputs=predictions)

The model is a conventional TensorFlow Keras model, and so tasks such as training and evaluation can use the functions offered by Keras. StellarGraph's data generators make it simple to construct the required Keras Sequences for input data.

# prepare the model for training with the Adam optimiser and an appropriate loss function
model.compile("adam", loss="categorical_crossentropy", metrics=["accuracy"])

# train the model on the train set
model.fit(generator.flow(train_targets.index, train_targets), epochs=5)

# check model generalisation on the test set
(loss, accuracy) = model.evaluate(generator.flow(test_targets.index, test_targets))
print(f"Test set: loss = {loss}, accuracy = {accuracy}")

This algorithm is spelled out in more detail in its extended narrated notebook. We provide many more algorithms, each with a detailed example.

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

StellarGraph is a Python 3 library and we recommend using Python version 3.6. The required Python version can be downloaded and installed from python.org. Alternatively, use the Anaconda Python environment, available from anaconda.com.

The StellarGraph library can be installed from PyPI, from Anaconda Cloud, or directly from GitHub, as described below.

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 as well as StellarGraph using pip execute the following command:

pip install stellargraph[demos]

The community detection demos require python-igraph which is only available on some platforms. To install this in addition to the other demo requirements:

pip install stellargraph[demos,igraph]

The StellarGraph library is available an Anaconda Cloud and can be installed in Anaconda Python using the command line conda tool, execute the following command:

conda install -c stellargraph stellargraph

First, clone the StellarGraph repository using git:

git clone https://github.com/stellargraph/stellargraph.git

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

cd stellargraph
pip install .

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

pip install .[demos]

StellarGraph is designed, developed and supported by CSIRO's Data61. If you use any part of this library in your research, please cite it using the following BibTex entry

@misc{StellarGraph,
  author = {CSIRO's Data61},
  title = {StellarGraph Machine Learning Library},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub Repository},
  howpublished = {\url{https://github.com/stellargraph/stellargraph}},
}

1. Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec. Neural Information Processing Systems (NIPS), 2017, (link webpage)

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. Attributed Network Embedding via Subspace Discovery. D. Zhang, Y. Jie, X. Zhu and C. Zhang, Data Mining and Knowledge Discovery, 2019, (link)

5. Graph Attention Networks. P. Veličković et al. International Conference on Learning Representations (ICLR), 2018, (link)

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

7. Simplifying Graph Convolutional Networks. F. Wu, T. Zhang, A. H. de Souza, C. Fifty, T. Yu, and K. Q. Weinberger. International Conference on Machine Learning (ICML), 2019, (link)

8. Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. H. Wu, C. Wang, Y. Tyshetskiy, A. Docherty, K. Lu, and L. Zhu. IJCAI 2019, (link)

9. Predict then propagate: Graph neural networks meet personalized PageRank. J. Klicpera, A. Bojchevski, A., and S. Günnemann, ICLR, 2019, arXiv:1810.05997.(link)

10. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. W. Chiang, X. Liu, S. Si, Y. Li, S. Bengio, and C. Hsiej, KDD, 2019, arXiv:1905.07953.(link)

11. Modeling relational data with graph convolutional networks. M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling, European Semantic Web Conference, 2018, arXiv:1609.02907 (link).

12. Complex Embeddings for Simple Link Prediction. T. Trouillon, J. Welbl, S. Riedel, É. Gaussier and G. Bouchard, ICML, 2016. (link)

13. Learning Structural Node Embeddings via Diffusion Wavelets. C. Donnat, M. Zitnik, D. Hallac, and J. Leskovec, SIGKDD, 2018, arXiv:1710.10321 (link)

14. Watch Your Step: Learning Node Embeddings via Graph Attention. S. Abu-El-Haija, B. Perozzi, R. Al-Rfou and A. Alemi, NIPS, 2018, arXiv:1710.09599 (link)

15. Deep Graph Infomax. P. Veličković, W. Fedus, W. L. Hamilton, P. Lio, Y. Bengio, R. D. Hjelm. International Conference on Learning Representations (ICLR), 2019, arXiv:1809.10341, (link).

16. Continuous-Time Dynamic Network Embeddings. Giang Hoang Nguyen, John Boaz Lee, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, and Sungchul Kim. Proceedings of the 3rd International Workshop on Learning Representations for Big Networks (WWW BigNet) 2018. (link)

17. Embedding Entities and Relations for Learning and Inference in Knowledge Bases. Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng, ICLR, 2015. arXiv:1412.6575 (link)

18. An End-to-End Deep Learning Architecture for Graph Classification. Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen, AAAI, 2018. (link)

19. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, and Haifeng Li. IEEE Transactions on Intelligent Transportation Systems, 2019. (link)