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).

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3
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0answers
16 views

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 ...
4
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1answer
80 views

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 ...
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1answer
34 views

How are edge features implemented in Geometric Deep Learning?

The work I've seen so far have the nodes containing features. Any resources for how to use a GCN on a graph where the edges are the ones that contain features rather than the nodes?
2
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0answers
29 views

Does GraphSage use hard attention?

I was reading the recent paper Graph Representation Learning via Hard and Channel-Wise Attention Networks, where the authors claim that there is no hard attention operator for graph data. From my ...
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0answers
25 views

What are the advantages of time-varying graph CNNs compared to fixed graph?

As I wrote in the title, what are the advantages of time-varying graph CNNs compared to fixed graph? For example, in CORA, which is a graph of citation relations of papers frequently used in graph CNN,...
1
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1answer
59 views

Is there a neural network method for time-varying directed graphs?

I want to study NN for time-varying directed graphs. However, as this field has developed relatively recently, it is difficult to find new ways. So the question is, is there any NN that can handle ...
2
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0answers
53 views

Why is graph convolution network in time-varying graphs useful for anomaly detection?

In this paper, the authors refer to the application of time-varying graphs as an open problem. And they say it will be useful for anomaly detection in financial networks, etc. But why is that useful?
2
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1answer
49 views

Examples of time-varying graph-structured data in real world

I'm looking for examples of time-varying graph-structured data for time-varying graph CNNs. First, I came up with the idea of infection network. Is there anything more? If possible, I want data that ...
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0answers
16 views

Random graph as input in geometric deep learning on time-varying graph

I want to create a framework that allows GDL to be applied to time-varying graphs. I came up with the Erdos-renyi model as an example of a time-varying graphs. GDL for graphs takes node information ...
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2answers
49 views

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

Both of them deal with data of graph structure like a network community. Is there a big difference there?
2
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0answers
33 views

What are the benefits of using the state information that maintains the graph structure?

When you applying a graph structured data to the graph convolution network, what are the benefits of using the state information that maintains the graph structure?
2
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1answer
49 views

What is the purpose and benefit of applying CNN to a graph?

I'm new to the graph convolution network. I wonder what is the main purpose of applying data with graph structure to CNN?
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0answers
81 views

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 ...
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1answer
43 views

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 ...
5
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1answer
68 views

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 ...
2
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0answers
31 views

Should I use the hyperbolic distance loss in the case of Poincarè Disk Model?

I trained a neural network which makes a regression to a Poincarè Disk Model with radius $r = 1$. I want to optimize using the hyperbolic distance $$ \operatorname{arcosh} \left( 1 + \frac{2|pq|^2|...
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2answers
2k views

What is non-Euclidean data?

What is non-Euclidean data? Where does this type of data arises? Apparently, graphs and manifolds are non-Euclidean data. Why exactly is that the case? What is the difference between non-Euclidean and ...
0
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0answers
31 views

How is the convolution operation used in CNNs a special case of the convolution operator?

How is the convolution operation used in convolutional neural networks (CNNs) a special case of the mathematical convolution operator? Most of us, when we think of the "convolution operation", we ...
1
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0answers
91 views

What is a graph neural network?

What is a graph neural network (GNN)? 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 ...
7
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2answers
553 views

What is geometric deep learning?

What is geometric deep learning (GDL)? How is it different from deep learning? Why do we need GDL? What are some applications of GDL?
8
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2answers
477 views

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 ...