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Table of Contents

This folder contains several examples of applying graph machine learning (ML) algorithms on network-structured data to solve several common problems including node attribute inference (inferring entity properties) and link prediction (predicting relations and/or their properties). The examples demonstrate using the StellarGraph library to build machine learning workflows on both homogeneous and heterogeneous networks.

Each folder contains one or more examples of using the StellarGraph implementations of the state-of-the-art algorithms, GraphSAGE [3], HinSAGE, GCN [5], GAT [6], Node2Vec [1], and Metapath2Vec [2]. GraphSAGE, HinSAGE, and GAT are variants of Graph Convolutional Neural networks [5]. Node2Vec and Metapath2Vec are methods based on graph random walks and representation learning using the Word2Vec [4] algorithm.

The examples folder structure is shown below.

  • /embeddings

    Examples of unsupervised node representation learning for homogeneous and heterogeneous networks, using the Node2Vec and Metapath2Vec algorithms.

  • /link-prediction-random-walks

    Examples of semi-supervised link prediction for homogeneous and heterogeneous networks, using the Node2Vec and Metapath2vec algorithms.

  • /link-prediction-graphsage

    Example of semi-supervised link prediction for a homogeneous network with attributed nodes, using the GraphSAGE algorithm.

  • /link-prediction-hinsage

    Example of supervised link attribute prediction for a heterogeneous network with attributed nodes of different types, using the HinSAGE algorithm.

  • /node-classification-graphsage

    Example of supervised node classification for a homogeneous network with attributed nodes, using the GraphSAGE algorithm.

  • /node-classification-gcn

    Example of semi-supervised node classification for a homogeneous network, using the GCN algorithm.

  • /node-classification-gat

    Example of supervised node classification for a homogeneous network with attributed nodes, using the GAT algorithm [6].

  • /node-classification-node2vec

    Example of unsupervised node representation learning using Node2Vec and supervised classification using the Scikit-learn library.

  • /node-classification-hinsage

    Example of semi-supervised node classification for a heterogeneous network with multiple node and link types, using the HinSAGE algorithm.

  • /calibration

    Examples of calibrating graph convolutional neural networks, e.g., GraphSAGE, for binary and multi-class classification problems.

References

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

  2. 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)

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

  4. Distributed representations of words and phrases and their compositionality. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. In Advances in Neural Information Processing Systems (NIPS), pp. 3111-3119, 2013. (link)

  5. Semi-Supervised Classification with Graph Convolutional Networks. T. Kipf, M. Welling. ICLR 2017. arXiv:1609.02907 (link)

  6. Graph Attention Networks. P. Velickovic et al. ICLR 2018 (link)

  7. On Calibration of Modern Neural Networks. C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger. ICML 2017. (link)