Tag Info

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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What is non-Euclidean data?

I presume this question was prompted by the paper Geometric deep learning: going beyond Euclidean data (2017). If we look at its abstract: Many scientific fields study data with an underlying ...
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What is non-Euclidean data?

Non-Euclidian geometry can be generally boiled down to the phrase the shortest path between 2 points isn't necessarily a straight line. Or, put in a way that lends itself very much to machine ...
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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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What is the difference between graph convolution in the spatial vs spectral domain?

Spectral Convolution In a spectral graph convolution, we perform an Eigen decomposition of the Laplacian Matrix of the graph. This Eigen decomposition helps us in understanding the underlying ...

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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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 is the best resources to learn Graph Convolutional Neural Networks?

I believe Graph Representation Learning book by William L. Hamilton is a great resource to start
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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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How does the K-dimensional WL test work?

I never used a k-WL in practice, but I did apply weisfeiler-lehman for my graph tasks. As you can know, the WL provides the coloring by interactive procedure that's assign each node a 'color' (...
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How do graph neural networks adapt to different number of nodes and connections of different graphs?

The essence of the reason, why this approach works for graphs with a different number of nodes is the locality and node order permutation invariance. The typical form of the layer-wise signal ...

What is a graph neural network?

Graph Neural Networks The term Graph Neural Network, in its broadest sense, refers to any Neural Network designed to take graph structured data as its input: To cover a broader range of methods, this ...
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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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What is the purpose and benefit of applying CNN to a graph?

There are some problems that involve graphs and manifolds (sometimes collectively called non-Euclidean data), such as molecule design and generation, drug repositioning, social networks analysis, ...
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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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Examples of time-varying graph-structured data in real world

You can take a look at traffic data for example if you follow link1, link2 you can find 3 publicly available traffic datasets which are already preprocessed. You cold also look at air quality ...
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What is the difference between graph convolution in the spatial vs spectral domain?

After I read multiple explanations from different sources I think I found the main difference between the two methods. Implementation wise the only difference is the matrix that you're multiplying the ...
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Understanding the node information score in the paper "Hierarchical Graph Pooling with Structure Learning"

Here, $H$ is a $n * d$ matrix where $n$ is the number of total nodes in the graph and $d$ is the dimension of embedding of each node. Using the notation in the question, the basic GNN formulation ...
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What are some conferences for publishing papers on graph convolutional networks?

Based on past publications, here are some journals and conferences where you can possibly publish or present a research paper on geometric deep learning or graph neural networks Neural Information ...
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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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How to represent and work with the feature matrix for graph convolutional network (GCN) if the number of features for each node is different?

The simplest way I could come with is to pad with 0 each feature which is not present. You said that you're going to add too much noise to the network, but I don't see the problem (please correct me ...
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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 Graph Convolutional Neural Networks forward propagate?

I think the picture you're presenting is mostly for educational purposes and that's why they are excluding the node itself from it's neighbors and using two distinct networks (most of the papers I've ...
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Why don't we use diffusion for non-graph CNNs?

Just for completeness, here is one simple formalization of a diffusion GCN (Gasteiger et al.): $\text{D-GCN}(X) = \sum_{k=1}^K A^k X W_k$ You have a diffusion factor $k \in [1 .. K]$ and you apply ...
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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}...
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How to pass batch of Graphs in GATv2Conv?

For efficiency reasons, it is the standard for graph learning libraries to use the format [n_batches x num_nodes, node_feature_dim], instead of having batches in an ...
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What is the best resources to learn Graph Convolutional Neural Networks?

There is also the proto-book Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges (2021), written by some of the experts on the topic. The book does not focus only on graphs and graph ...
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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 ...
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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 ...
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How are edge features implemented in Geometric Deep Learning?

In the paper Neural Message Passing for Quantum Chemistry (2017), the authors (from Google, Google Brain and Google DeepMind) introduce a framework called message passing neural network (MPNN), which ...
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