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What is geometric deep learning?

To complete the first answer that is rather graph oriented, I will write a little about deep learning on manifolds, which is quite general in terms of GDL thanks to the nature of manifolds. Note ...
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Accepted

What is geometric deep learning?

The article Geometric deep learning: going beyond Euclidean data (by Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst) provides an overview of this relatively new sub-...
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Is there an open-source implementation for graph convolution networks for weighted graphs?

You can use Pytorch_Geometric library for your projects. Its supports weighted GCNs. It is a rapidly evolving open-source library with easy to use syntax. It is mentioned in the landing page of ...
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Is there a neural network method for time-varying directed graphs?

I'm seeing recent trend of combining RNN/CNN with GNN(graph neural networks) so that both time dependency and topology are captured. I would suggest you to start by looking at DCRNN (Yaguang Li et al.)...
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Machine learning with graph as input and output

You can flatten the graph into a matrix and then train it like a normal neural network input. Perhaps an adjacency graph or maybe simply a series of linear equations representing the nodes and convert ...
Accepted

What benefits can be got by applying Graph Convolutional Neural Network instead of ordinary CNN?

Generally speaking a graph CNN is applied to data represented by graphs, not images. a graph is a collection of nodes and edges connecting them. an image is a 2D or 3D matrix, in which each element ...
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What benefits can be got by applying Graph Convolutional Neural Network instead of ordinary CNN?

Bioinformatics is an area that Graph Convolutional Neural Network is useful. Consider protein networks, or gene-gene networks. Surely, the biological networks can be represented as a graph. Now, you ...
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How to solve peg solitaire with a graph search?

Your approach seems reasonable to me. The edges do not necessarily have to be numbers, but, if you wish, you could also encode the actions as numbers. For example, the weight of an edge could ...
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Are there neural networks that accept graphs or trees as inputs?

Yes, there are numerous, coming under the umbrella term Graph Neural Networks (GNN). The most common input structures accepted by these techniques are the adjacency matrix of the graph (optionally ...
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Accepted

Is there an open-source implementation for graph convolution networks for weighted graphs?

A Comprehensive Survey on Graph Neural Networks (2019) presents a list of ConvGNN's. All of the following accept weighted graphs, and three accept those with edge weights as well: And below is a ...
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How can I learn a graph given nodes with features in a supervised fashion?

It's perfectly reasonable to apply 'traditional' Deep Learning approaches to try and learn an adjacency matrix (a matrix is just a vector of vectors, which can be flattened into a single output vector)...
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• 1,148
1 vote

Neural network for data visualization

I doubt that this problem is a good application of NN. Take a look at this https://en.wikipedia.org/wiki/Force-directed_graph_drawing. And try Graphviz if you haven't already. Drawing graphs in a ...
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1 vote

Neural network for data visualization

Neural networks are used to visualize high dimensional data through the use of autoencoding. It's similar to Principal Component Analysis and is regarded to perform better then PCA. Autoencoding will ...

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