Graph neural networks (GNNs) are connectionist models that capture the dependence of graphs via message passing between the nodes of graphs. Unlike standard neural networks, graph neural networks retain a state that can represent information from its neighborhood with arbitrary depth.

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

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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.

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Introduction to graph representation learning, including methods for embedding graph data, graph neural networks, and deep generative models of graphs.

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

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2020-06-14 · Introduce a series of effective graph-structured networks, including non-local neural networks, spatial generalized propagation networks, etc.

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

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

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Geometric deep learning extension library for PyTorch.

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Python package built to ease deep learning on graph, on top of existing DL frameworks.

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State-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data.

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Efficient and Friendly Graph Neural Network Library for TensorFlow 1.x and 2.x.

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

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

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2020-05-14 · Little Ball of Fur is a graph sampling extension library for NetworkX.

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Graph Neural Networks with Keras and Tensorflow 2.

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2020-03-03 · A general purpose community detection and network embedding library for research built on NetworkX.

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