Questions tagged [graph-neural-networks]

For questions related to graph neural networks, which are artificial neural networks applied to graphs.

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3answers
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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?
15
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4answers
9k views

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. ...
5
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1answer
734 views

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 ...
5
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2answers
1k views

Machine learning with graph as input and output

In my application, I have inputs and outputs that could be represented as graphs. I have a number of acceptable pairs of input and output graphs. I want to use these to train a model. I am looking ...
4
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2answers
380 views

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 ...
4
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2answers
406 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 ...
3
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1answer
171 views

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 ...
3
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1answer
116 views

Are Graph Neural Networks generalizations of Convolutional Neural Networks?

In lecture 4 of this course, the instructor argues that GNNs are generalizations of CNNs, and that one can recover CNNs from GNNs. He presents the following diagram (on the right) and mentions that it ...
3
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1answer
124 views

How can we derive a Convolution Neural Network from a more generic Graph Neural Network?

Convolution Neural Network (CNNs) operate over strict grid-like structures ($M \times N \times C$ images), whereas Graph Neural Networks (GNNs) can operate over all-flexible graphs, with an undefined ...
2
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1answer
53 views

Understanding the node information score in the paper "Hierarchical Graph Pooling with Structure Learning"

The paper Hierarchical Graph Pooling with Structure Learning (2019) introduces a distance measure between: a graph's node-representation matrix $\text{H}$, and an approximation of this constructed ...
2
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0answers
53 views

Are spectral approaches to Graph Neural Networks still considered?

I've been reading several papers and reviews about Graph Neural Networks, and I still feel a bit confused about the difference between the two approaches, and also if the spatial approaches have ...
2
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0answers
52 views

Reinforcement learning and Graph Neural Networks: Entropy drops to zero

I am currently working on an experiment to link reinforcement learning with graph neural networks. This is my architecture: Feature Extraction with GCN: there is a fully meshed topology with ...
2
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0answers
33 views

Graph Neural Networks: Quesitons about different GCN Architectures

This might be moreof a question about nested function classes: For k class node classification in a graph with n nodes, and d feature vector. I want to compare Architecture I: the GCN model of Kipf/ ...
2
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0answers
91 views

Why can we perform graph convolution using the standard 2d convolution with $1 \times \Gamma$ kernels?

Recently I was reading this paper Skeleton Based Action RecognitionUsing Spatio Temporal Graph Convolution. In this paper, the authors claim (below equation (\ref{9})) that we can perform graph ...
2
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0answers
21 views

How do CNNs or RNNs "stack the feature of nodes by a specific order"?

I am trying to understand the following statement taken from the paper Graph Neural Networks: A Review of Methods and Applications (2019). Standard neural networks like CNNs and RNNs cannot handle ...
2
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0answers
19 views

How should I deal with multi-dimensional tensors for nodes in a graph convolution network?

How to work with GCN when the features of each node is not a 1D vector? For example, if the graph has N nodes and each node has features of the form $C \times D \times E$. Also, is there an open-...
1
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1answer
103 views

How Graph Convolutional Neural Networks forward propagate?

In the basic variant of GCN we have the following: Here we aggregate the information from the adjacent node and pass it to a neural network, then transform our own information and add them all. But ...
1
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1answer
48 views

What are some conferences for publishing papers on graph convolutional networks?

What are some conferences for publishing papers on graph convolutional networks?
1
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1answer
93 views

Is there a graph neural network algorithm that can deal with a different number of input and output nodes?

I am new to graph neural networks and their applications. I have an input graph $G = \{V, E\}$ and an output graph $G' = \{V', E'\}$ where the number of nodes $V$ and $V'$ are different. I am trying ...
1
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1answer
34 views

How does a GCN handle new input graphs?

Quick questions to see whether I understand GCNs correctly. Is it correct that, if I have trained a GCN, it can take arbitrary graphs as input, assuming the feature size is the same? I can't seem to ...
1
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0answers
10 views

What is the reason behind using node embeddings?

I was reading Chapter 3 from the following book (here) on graph representation learning. The chapter is about node embeddings. Question: What is the point of using node embeddings? Do we use them: to ...
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0answers
10 views

What are examples of node 'features' in graph networks?

Context: I was reading Chapter 3 in the following book (here) about graph representation learning. Before I get to node embeddings, I wanted to make sure that I do understand what is meant by the ...
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0answers
48 views

How do convolutional layers of basic Graph Convolutional Networks work?

I was reading the following article on Towards Data Science (here) and it says the following, regarding the calculation of convolutional layers: So the overall steps are: Transform the graph into ...
1
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2answers
33 views

Can I extend Graph Convolutional Networks to graphs with weighted edges?

I'm researching spatio-temporal forecasting utilising GCN as a side project, and I am wondering if I can extend it by using a graph with weighted edges instead of a simple adjacency matrix with 1's ...
1
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1answer
26 views

Why the non-exploitation of edge labels in current graph convolutions "results in an overly homogeneous view of local graph neighborhoods"?

I am currently reading a paper called Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (2017, CPPR), and I cannot understand the following sentence: We identify that the ...
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0answers
24 views

Spectral Networks and Deep Locally Connected Networks on Graphs

I’m reading the paper Spectral Networks and Deep Locally Connected Networks on Graphs and I’m having a hard time understanding the notation shown in the picture below (the scribbles are mine): ...
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0answers
88 views

Can I think graph convolution as 2D convolution like images?

Kipf et al described in his paper that we can write graph convolution operation like this: $$H_{t+1} = AH_tW_t$$ where, $A$ is the normalized adjacency matrix, $H_t$ is the embedded representation of ...
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0answers
23 views

How to design a graph neural network to predict the forces in truss elements of a space frame?

I am trying to create a Graph NN that will be able to predict the forces in truss elements of a space frame. The input for the NN will be a graph, where the nodes represent the nodes of the spaceframe....
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0answers
29 views

How to learn how to select a subgraph via reinforcement learning?

I have the following problem. I am given a graph with a lot (>30000) nodes. Nodes are associated with a low (<10)-dimensional feature vector, and edges are associated with a low (<10)-...
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0answers
17 views

How to estimate the convolutional representation of a graph from its similarity to other graph convolutional representation?

Suppose we have two graphs A and B disconnected to each other (let's say 2-hops each), within a larger graph. If the convolutional representation of graph A is known, is it possible to estimate the ...
0
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1answer
77 views

K dimensional WL test in K-WL GCN

I am reading a paper on the K-WL GCN. I did not complete the paper yet but I just skimmed over it. There I am trying to understand the K-WL test (page 3 Weisfeiler-Leman Algorithm). I think my ...
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0answers
14 views

How does graph Fourier transform work when multiple signals present on each node?

Context: I was reading the following set of notes (page 83): here and it says: Thus, the Fourier transform of signal (or function) $ \mathbf{f} \in R^{|V|} $ on a graph can be computed as $$ \mathbf{...
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0answers
34 views

How does Chebyshev approximation of spectral convolution work?

I was reading the following paper: here. In it, it talks about spectral graph convolutions and says: We consider spectral convolutions on graphs defined as the multiplication of a signal $x \in R^N$ (...
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0answers
15 views

Question about eigenvectors of Laplacian matrix in relation to graph Fourier transform?

(I have copied this question from my post on maths stack exchange as I don't think it was getting much/any traction there) I am reading the following book/notes on Graph Representation Learning (here) ...
0
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0answers
13 views

Graph neural network - what level (node or link or graph) prediction should be used for my problem?

I posted this on cross-validated but did not get a response. Trying my luck here. Sorry if this is not recommended. I have an undirected graph with nodes separated within a specified distance, say d, ...
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0answers
17 views

Is graph embedding linear in its maintaining of graph geometry?

It is claimed that the main goal of graph embedding methods is to pack every node's properties into a vector with a smaller dimension, so node similarity in the original complex irregular spaces can ...
0
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0answers
9 views

Does it makes sense to use directed graphs for node classification tasks in graph neural network?

I was wondering if it does makes sense or not to use directed graphs for node classification tasks in graph neural network. Most of the successful methods for node classification tasks are the ones ...
0
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0answers
18 views

Are there any good references that describe the equations of the forward pass of Graph Neural Networks?

I am trying to program Graph Neural Network from scratch. Can the community please suggest a good reference/s to read about the equations of the forward pass in Graph Neural Networks, especially in ...
0
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0answers
10 views

How exactly is masking performed in the training part of the paper "Semi-Supervised Classification with Graph Convolutional Networks"?

I am struggling to understand the training part of the paper Semi-Supervised Classification with Graph Convolutional Networks (2017) by Thomas Kipf and Max Welling. The GitHub repo is here. I do not ...
0
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0answers
21 views

Which well known node embedding algorithms to use for weighted graphs?

I am looking for a node representation learning algorithm to generate node embeddings that supports weighted graphs. I modified GCN to support weighted graphs, but I want to know an algorithm that ...
0
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0answers
11 views

how to use Laplacian of mesh structure (LBO) for meshes that are registered in deep learning methods based on spectrum (ChebNet for isntance)?

In graph neural network frameworks, there is always a template with a shared structure among all graphs. I have meshes that are registered but obviously, Lalpalcian and their geometry are different. ...
0
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0answers
39 views

How is it possible that the softmax combined with the MSE in a molecule classification task performs than than the cross-entropy?

I'm working on a GNN project associated with molecule classification. The project is to classify if the atom in the molecule will initiate a certain reaction. For example, a molecule can be ...
0
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0answers
33 views

What are the methods for constructing GNN input feature vectors?

Which popular methods exist to construct input feature vectors X from the semantic information stored in Knowledge Graph? In my case, the nodes are 2-3 tokens of text; edges are multi-relation. The ...
0
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0answers
22 views

Sparse Multi-hot encoding and autoencoders

I'm working with graph neural networks. I have a large graph. Each node has 4 features [A,B,C,D]: 2 categorical with high cardinality: 86k (A) and 148k (B) different features 2 integer with ranges: [...
0
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0answers
18 views

How to deal with dynamically changing input tensor in neural networks without padding?

I have a dataset about the monitored health/growth of a community of people. The dataset has tensor shaped (batch_size, features, person, window), where: person==...
0
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0answers
113 views

How can feedforward neural networks act as contraction maps?

In graph neural networks, the Banach fixed-point theorem and Jacobi method it is described that the transition from one state to another be defined by a contraction map with a fixed-point. The autor ...