Artificial Intelligence Stack Exchange
Artificial Intelligence Stack Exchange

Questions tagged [geometric-deep-learning]

For questions related to geometric deep learning, which is the application of deep learning techniques to non-Euclidean data (e.g. graphs and manifolds).

For more info, have a look at this question What is geometric deep learning? or the paper Geometric deep learning: going beyond Euclidean data (2017) by Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst.

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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. Apparently, graphs and manifolds are non-Euclidean data.…
nbro
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What is geometric deep learning?

What is geometric deep learning (GDL)? Here are a few sub-questions How is it different from deep learning? Why do we need GDL? What are some applications of GDL?
nbro
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What is the difference between graph convolution in the spatial vs spectral domain?

I've been reading different papers regarding graph convolution and it seems that they come into two flavors: spatial and spectral. From what I can see the main difference between the two approaches is that for spatial you're directly multiplying the…
razvanc92
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What benefits can be got by applying Graph Convolutional Neural Network instead of ordinary CNN?

What benefits can we got by applying Graph Convolutional Neural Network instead of ordinary CNN? I mean if we can solve a problem by CNN, what is the reason should we convert to Graph Convolutional Neural Network to solve it? Are there any examples…
piratesailor
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Is there an open-source implementation for graph convolution networks for weighted graphs?

Currently, I'm using a Python library, StellarGraph, to implement GCN. And I now have a situation where I have graphs with weighted edges. Unfortunately, StellarGraph doesn't support those graphs I'm looking for an open-source implementation for…
port trum
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Do you know any examples of geometric deep learning used in industry?

I'm interested in the industrial use of GDL (see https://arxiv.org/abs/1611.08097). Is it used in industry? That is, does any company have access to non-Euclidean data and process it directly instead of converting it to a more standard format?
Guillermo Mosse
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Are generative models actually used in practice for industrial drug design?

I just finished reading this paper MoFlow: An Invertible Flow Model for Generating Molecular Graphs. The paper, which is about generating molecular graphs with certain chemical properties improved the SOTA at the time of writing by a bit and used a…
Adriaan
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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 outputs of GNNs? How can GNNs be trained? Can we…
nbro
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Relevance of Weisfeiler–Lehman Graph Isomorphism Test limitation for Graph Neural Networks

Graph Neural Networks power is limited by the power of Weisfeiler–Lehman Graph Isomorphism algorithm. Quoting wikipedia: It has been demonstrated that GNNs cannot be more expressive than the Weisfeiler–Lehman Graph Isomorphism Test. In practice,…
Rexcirus
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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 vague understanding of how to perform…
Swakshar Deb
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How should I handle different input sizes in graph convolution networks?

I'm a student beginning to study deep learning, and would like to practice with a simple project using a Graph Convolutional Network. However, I'm not quite sure how to handle different input sizes of graphs for the GCN. How would I do this? Is…
Newbie1111
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What are the exact meaning of "lower-order structure" and "higher-order structure" in this paper?

I recently read a paper on community detection in networks. In the paper EdMot: An Edge Enhancement Approach for Motif-aware Community Detection, the authors consider the "lower-order structure" of the network at the level of individual nodes and…
hichewness
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What is the difference between GAT and GaAN?

I was looking at two papers Graph Attention Networks (GAT) by Petar Veličković and GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs by Jiani Zhang. I'm trying to implement the second paper and I'm having some…
razvanc92
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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 between the nodes of the graph or tree?
user8426627
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How do graph neural networks adapt to different number of nodes and connections of different graphs?

I have recently been studying GNN, and the fundamental idea seems to be the aggregation and transfer of information from a node's neighborhood to update the node's internal state. However, there are few sources that mention the implementation of GNN…
tangolin
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