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Questions tagged [graph-neural-networks]

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

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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. ...
nbro's user avatar
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18 votes
3 answers
5k views

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's user avatar
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17 votes
2 answers
11k 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 ...
razvanc92's user avatar
  • 1,148
7 votes
3 answers
2k 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 ...
port trum's user avatar
5 votes
2 answers
2k 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 ...
Suresh's user avatar
  • 159
5 votes
1 answer
2k 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 ...
nbro's user avatar
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4 votes
2 answers
705 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 ...
Swakshar Deb's user avatar
4 votes
2 answers
1k 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 ...
user8426627's user avatar
4 votes
1 answer
400 views

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 ...
Rexcirus's user avatar
  • 1,174
3 votes
1 answer
3k views

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 ...
tangolin's user avatar
  • 153
3 votes
1 answer
112 views

How can I improve this toy Graph Neural Network Generative Language model [closed]

Background I'm an undergraduate student with research interests in a field of physics that has significant overlap with graph theory, and a functioning knowledge of how simple neural nets work and how ...
MomentumEigenstate's user avatar
3 votes
1 answer
801 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 ...
An Ignorant Wanderer's user avatar
3 votes
2 answers
417 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 ...
Kris's user avatar
  • 39
2 votes
1 answer
161 views

Node classification with random labels for GNNs

I decided to train GCN on the Cora dataset for the node classification task, however, with the random labels, i.e., applying np.random.shuffle(labels). For the ...
RobJan's user avatar
  • 123
2 votes
1 answer
574 views

Using GraphSAGE model for multigraph datasets

I checked out applications of GraphSAGE and it seems like its primarily used for single graph datasets. For example - Cora dataset - Its one big graph with 2708 nodes and 5429 edges. The model can ...
Prince Bhatti's user avatar
2 votes
1 answer
2k views

How does the K-dimensional WL test work?

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 ...
Swakshar Deb's user avatar
2 votes
1 answer
64 views

How to pass batch of Graphs in GATv2Conv?

I have a batched input to GATv2Conv with node matrix of shape [batch_sz , num_nodes , node_feature_dim] , but the GATv2Conv accepts input of dim 2 ,searching through the internet , I found some ...
Sarvagya Porwal's user avatar
2 votes
1 answer
235 views

Why don't we use diffusion for non-graph CNNs?

I'm pretty new to graph neural networks, so please forgive me if this is a silly question. Diffusion is a method used to improve graph CNNs, however it seems to me that general CNNs can also benefit ...
xuq01's user avatar
  • 23
2 votes
1 answer
455 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 ...
Swakshar Deb's user avatar
2 votes
1 answer
69 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 ...
brazofuerte's user avatar
  • 1,031
2 votes
1 answer
37 views

How does Training and Validating work with Graph CNNs

I'm training a Graph Convolutional Neural Network to output embeddings for nodes that I eventually want to perform classification on. I am a little confused on how the training, validation, and ...
Kiran Manicka's user avatar
2 votes
1 answer
158 views

Why readout operation in message passing graph neural nets have to be invariant to node permutations?

I am reading the paper Neural Message Passing for Quantum Chemistry by Justin Gilmer et al. And I have a question regarding this passage The message functions $M_t$, vertex update functions $U_t$, ...
dmasny's user avatar
  • 23
2 votes
0 answers
149 views

What is the difference between Probabilistic Graphical models and Graph Neural networks?

While going over PGMs and GNNs, it seems like both leverage the graph data structure. The former has been used to represent causal associations (among other things), while the latter has a varied set ...
desert_ranger's user avatar
2 votes
1 answer
1k 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 ...
Rocky the Owl's user avatar
2 votes
0 answers
235 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 ...
James Arten's user avatar
2 votes
0 answers
103 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 ...
Micha Christ's user avatar
2 votes
0 answers
65 views

Given a 2-layer GCN, can we choose the dimensions of the 2nd weight matrix, such that this architecture has the same capacity as a 1-layer GCN?

This might be more of 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 ...
Tinatim's user avatar
  • 121
2 votes
0 answers
143 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 ...
Swakshar Deb's user avatar
2 votes
0 answers
22 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 ...
Jacob B's user avatar
  • 247
2 votes
0 answers
22 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-...
shivam's user avatar
  • 21
1 vote
1 answer
320 views

What is a filter in the context of graph convolutional networks?

In Section 2.1 of the research paper titled Semi-Supervised Classification with Graph Convolutional Networks by Thomas N. Kipf et al., Spectral convolution on graphs defined as The multiplication of ...
JAEMTO's user avatar
  • 125
1 vote
1 answer
1k views

How are GCN doing semi-supervised learning?

In Semi-Supervised Classification with Graph Convolutional Networks, the authors say that GCN is an approach for semi-supervised learning (SSL). But a GCN is making predictions using only the graph ...
willtryagain's user avatar
1 vote
2 answers
196 views

How to do image classification with optional metadata?

I have a vanilla image classification problem. The image may optionally have some numerical metadata associated with it. We don't assume uniform availability of this metadata, i.e., the model should ...
Vardaan Pahuja's user avatar
1 vote
1 answer
268 views

Why is there a shared matrix W in graph attention networks instead of the query-key-value trio like in regular transformers?

In section 2.1 of the Graph attention network paper The graph attention layer is described as as an initial step, a shared linear transformation, parametrized by a weight matrix, W ∈ RF ′×F , is ...
oliver.c's user avatar
  • 135
1 vote
1 answer
227 views

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

What are some conferences for publishing papers on graph convolutional networks?
port trum's user avatar
1 vote
1 answer
35 views

GREED - preservation theoretical properties in the GED(graph edit distance) pridiction

In this paper "GREED: A Neural Framework for Learning Graph Distance Functions", function F is defined to satisfy metric property and triangle inequality property. I wonder how can I prove ...
GH HONG's user avatar
  • 13
1 vote
1 answer
164 views

Is there a neural network method to encode a directed graph?

I want to do a graph classification task. Those graphs are directed, and their edges have features. I knew little about graph representation methods, but I did some research, and find most of the ...
han's user avatar
  • 11
1 vote
1 answer
440 views

Rationalle behind SE3 Transformer?

I have just finished reading the SE3 transformer paper (SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks) by Fuchs et-al and although I'm sure I understand less than 50% of the ...
Protostome's user avatar
1 vote
1 answer
202 views

How to use structural information in a Transformer?

I am performing a Neural Machine Translation (NMT) task. In my case, input data has relational information. I know I can use a Graph Neural Network (GNN) and use a Graph2Seq model. But I can't find a ...
Exploring's user avatar
  • 353
1 vote
1 answer
659 views

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

What kind of features does each node have as an input graph to a graph neural network? For example, we want to do image classification with GNN, what are the features of each pixel? Or if anyone could ...
selin's user avatar
  • 11
1 vote
1 answer
660 views

Is "node embedding" in GNN analogous to "hidden layer" of FFN?

So in Graph Neural Network (GNN) we have node embeddings which is a feature vector that describes the node, is it analogous to hidden layer of Artificial neural network such as feed-forward neural ...
user0193's user avatar
  • 145
1 vote
1 answer
122 views

Graph Convolutional Networks: why are non-parametric filters not localized in space?

I was reading the following paper here about some of the groundwork in graph deep learning. On page 3, in the bit entitled Polynomial parameterization for localized filters, it states that non-...
Rocky the Owl's user avatar
1 vote
1 answer
427 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 ...
Rocky the Owl's user avatar
1 vote
1 answer
339 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 ...
shunyo's user avatar
  • 133
1 vote
1 answer
342 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 ...
Nikita Makarov's user avatar
1 vote
0 answers
221 views

How to get ZINC 500k dataset?

I have been using the ZINC graph regression dataset available through pytorch geometric datasets for a while now in two of its modes (12k examples and 250k examples). However, in the PapersWithCode ...
Angelo's user avatar
  • 211
1 vote
0 answers
24 views

Is there another type of NN that can capture just the structure of the graph?

I have a classification problem where the inputs are graphs, with no special features in the nodes of the graph. I tried to use message passing layers like GCN and GIN but they were not able to ...
Hadar Shavit's user avatar
1 vote
1 answer
189 views

Are there any advantages of encoding an image as a graph to use in Graph Convolutional Networks?

I have seen this encoding of an image as a graph: The set of the nodes $V$ is the set of pixels. If the image is of size $10\times10$, then we have $10\cdot10=100$ pixels. Each node has a length 3 ...
puradrogasincortar's user avatar
1 vote
1 answer
844 views

Are there Explainable GNN methods for node regression tasks?

I am wondering if there are gnn explainable methods for a regression task (e.g., traffic forecasting) where nodes have numerical features and the predicted output is a numerical value. Most of ...
Achiles Br's user avatar
1 vote
1 answer
398 views

How does Weight Sharing with the Generalization in Graph Neural Networks work?

I have two closely related points regarding the weight sharing and generalization of graph Neural network. For illustration purposes, I attached two images which I reference. Images are taken from the ...
Imago's user avatar
  • 111