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19 votes

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 ...
Blupon's user avatar
  • 301
8 votes
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-...
nbro's user avatar
  • 40.9k
4 votes

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 ...
user1825567's user avatar
3 votes
Accepted

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.)...
razvanc92's user avatar
  • 1,148
3 votes

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 ...
user3551708's user avatar
3 votes
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 ...
user12075's user avatar
  • 330
2 votes

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 ...
SmallChess's user avatar
  • 1,411
2 votes

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 ...
nbro's user avatar
  • 40.9k
2 votes

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 ...
brazofuerte's user avatar
  • 1,031
2 votes
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 ...
brazofuerte's user avatar
  • 1,031
2 votes

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)...
NietzscheanAI's user avatar
2 votes
Accepted

Relevance of Weisfeiler–Lehman Graph Isomorphism Test limitation for Graph Neural Networks

Firstly, as already stated in the Wikipedia quote: Observing that a type of GNN is as expressive as the Weisfeiler–Lehman (WL) Test, means in practice that two graphs $\mathcal{G}_1$ and $\mathcal{G}...
Chillston's user avatar
  • 1,748
1 vote

What kind of features does each node have as an input graph to a graph neural network?

Applying GNNs to images can be realized in different ways. If you only substitute a visual ConvNet by a GNN, then the pixel values would be the same as what goes into a ConvNet. The only difference ...
Chillston's user avatar
  • 1,748
1 vote

How does a GCN handle new input graphs?

Graph neural networks, of which GCNs are a specific type, are able to handle arbitrary graphs as input. GNNs operate first over "neighborhoods" of nodes to compute individual node ...
primussucks's user avatar
1 vote
Accepted

What exactly is the eigenspace of a graph (in spectral clustering)?

In spectral clustering we not find the eigenvectors of a graph (a graph is not a matrix) but the eigenvalues/eigenvectors of the Laplacian matrix related to the adjacency matrix of the graph: graph =&...
pasaba por aqui's user avatar
1 vote

What is Precision@K for link prediction in graph embedding meaning?

I understand the confusion and I wanted to refer to this (older post) because the metric really is unclear in the context of the SDNE paper. Perhaps I can try to explain it for future readers, in ...
IMA's user avatar
  • 111
1 vote
Accepted

What is Precision@K for link prediction in graph embedding meaning?

These measures are used for evaluating how "good" an embedding of a graph is or how "good" the graph reconstructed from the embedding resembles the original. Given the embedding ...
Varun Vejalla's user avatar
1 vote

How to solve the problem of variable-sized AST as input for a (convolutional) neural network model?

You discovered already one solution for your problem: Zero-Padding. There are two other common possibilities: Using Recurrent NNsThis is often used at text processing, where you feed each word one ...
Luca Jung's user avatar
  • 121
1 vote

What is the difference between graph semi-supervised learning and normal semi-supervised learning?

The authors of your cited paper use the term graph-based semi-supervised learning (G-SSL) to refer to semi-supervised learning techniques which take graph structured data as their input. Given their ...
brazofuerte's user avatar
  • 1,031
1 vote

What are the differences between network analysis and geometric deep learning on graphs?

Both study properties of a network. The literature under respective titles seems to focus on certain topics. Network analysis seems to focus on understanding the structure of a network. Centrality , ...
Kiko's user avatar
  • 11
1 vote
Accepted

What are the differences between network analysis and geometric deep learning on graphs?

Network analysis does not necessarily use deep learning techniques, while geometric deep learning (GDL) on graphs uses only deep learning techniques (that is, you train a neural network using gradient ...
nbro's user avatar
  • 40.9k
1 vote

What are the exact meaning of "lower-order structure" and "higher-order structure" in this paper?

Low order/low level information refers to the most granular level of information. This is the most informative in terms of volume of information, but it can often be difficult to conceptualise for ...
brazofuerte's user avatar
  • 1,031
1 vote

Does GraphSage use hard attention?

GraphSage does not have attention at all. Yes, it randomly samples (not most important as you claim) a subset of neighbors, but it does not compute attention score for each neighbor.
kkonevets's user avatar
1 vote

Are there neural networks that accept graphs or trees as inputs?

There are types of neural networks designed exactly for that purpose. For example, graph convolutional networks (GCN) by Thomas N. Kipf. The input to the network will be a matrix of size $N \times F$, ...
razvanc92's user avatar
  • 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 ...
hellmean's user avatar
  • 140
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 ...
Default picture's user avatar

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