Questions tagged [graph-neural-networks]

For questions related to graph neural networks, which are artificial neural networks applied to graphs.

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What is a graph neural network?

What is a graph neural network (GNN)? Here are some sub-questions How is a GNN different from a NN? How exactly is a GNN related to graphs? What are the components of a GNN? What are the inputs and ...
nbro's user avatar
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What is non-Euclidean data?

What is non-Euclidean data? Here are some sub-questions Where does this type of data arise? I have come across this term in the context of geometric deep learning and graph neural networks. ...
nbro's user avatar
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What is the best resources to learn Graph Convolutional Neural Networks?

For the past few days, I am trying to learn graph convolutional networks. I saw some of the lectures on youtube. But I can not able to get any clear concept of how those networks are trained. I have a ...
Swakshar Deb's user avatar
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Are there neural networks that accept graphs or trees as inputs?

As far I know, the RNN accepts a sequence as input and can produce as a sequence as output. Are there neural networks that accept graphs or trees as inputs, so that to represent the relationships ...
user8426627's user avatar
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How can we derive a Convolution Neural Network from a more generic Graph Neural Network?

Convolution Neural Network (CNNs) operate over strict grid-like structures ($M \times N \times C$ images), whereas Graph Neural Networks (GNNs) can operate over all-flexible graphs, with an undefined ...
Kris's user avatar
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Can I think of the graph convolution operation as a regular 2D convolution for images?

Kipf et al. described in his paper that we can write graph convolution operation like this: $$H_{t+1} = AH_tW_t$$ where $A$ is the normalized adjacency matrix, $H_t$ is the embedded representation of ...
Swakshar Deb's user avatar