Collections of ML on graphs.

graph-neural-networks graph-convolutional-networks autoencoders graphs

2020-03-30 · A breakdown of the inner workings of GNNs.

graph-neural-networks graph-deep-learning graphs illustrated

2020-04-23 · Novel approaches based on the theme of structuring the representations and computations of neural network-based models in the form of a graph.

graph-neural-networks graph-convolutional-networks graph-auto-encoders relational-graph-convolutional-networks

2019-12-04 · A Comprehensive Survey on Graph Neural Networks.

graph-neural-networks graph-convolutional-networks graph-autoencoders spatial-temporal-gnns

A pytorch adversarial library for attack and defense methods on images and graphs.

adversarial-learning adversarial-attacks adversarial-defense pytorch

2020-07-20 · Does depth in graph neural network architectures bring any advantage?

graph-neural-networks depth graphs tutorial

2020-07-27 · In this post, we describe Temporal Graph Network, a generic framework for deep learning on dynamic graphs.

graph-neural-networks temporal-graph-networks graphs article

2019-11-13 · General tool for explaining predictions made by graph neural networks (GNNs).

graph-neural-networks interpretability explainability graphs

2020-05-07 · We propose a simple framework (GraphEDM) and a comprehensive Taxonomy to review and unify several graph representation learning methods.

graph-neural-networks graph-convolutional-networks autoencoders graph-regularization

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