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

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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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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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2020-04-20 · I’ll talk about my experience on how to build and train Graph Neural Networks (GNNs) with JAX.

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2020-04-07 · Source code for the paper: ProteinGCN: Protein model quality assessment using Graph Convolutional Networks.

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

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

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Geometric deep learning extension library for PyTorch.

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

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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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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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2020-04-07 · Source code for the paper: ProteinGCN: Protein model quality assessment using Graph Convolutional Networks.

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2019-11-11 · ASAP is a sparse and differentiable pooling method that addresses the limitations of previous graph pooling layers.

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

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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.

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2019-11-11 · ASAP is a sparse and differentiable pooling method that addresses the limitations of previous graph pooling layers.

graph-neural-networks graph-classification pool self-attention

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

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

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2020-05-09 · Equivariant Mesh Neural Networks and Neural Augmented (Factor) Graph Neural Networks.

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