Graph convolutional networks (GCN)


Topic | v1 | created by janarez |
Description

Generalization of neural networks to arbitrary graphs.


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subtopic of Deep learning

Deep learning (also known as deep structured learning) is part of a broader family of machine learnin...

uses PyTorch geometric

PyTorch Geometric (PyG) is a geometric deep learning extension library for PyTorch. It consists of...

uses Spektral

Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The ma...

relates to Graph isomorphism problem

The graph isomorphism problem is the computational problem of determining whether two finite graphs a...


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Resources

compared in How Powerful are Graph Neural Networks?

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Despite GNNs revolutionizing graph representation learning, there is limited understanding of their r...

cons given in How powerful are Graph Convolutions? (review of Kipf & Welling, 2016)

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This post is about a paper that has just come out recently on practical generalizations of convolutio...

treated in Graph convolutional networks

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Many important real-world datasets come in the form of graphs or networks: social networks, knowledge...

treated in Geometric Deep Learning

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This website represents a collection of materials in the field of Geometric Deep Learning. We collect...

discussed in A Comprehensive Survey on Graph Neural Networks

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There is an increasing number of applications where data are generated from non-Euclidean domains and...