Questions tagged [graph-neural-networks]

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

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Can Graph Neural network leverage only the topological structure?

Graph Neural Networks (GNNs) are a powerful tool that allow learning on graphs by leveraging both the topological structure and the feature information for each node. For the particular problem I am ...
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Cause of randomness in AUC score for GNN

I have implemented a GraphSAGE model using dgl for link prediction. On average the auc score of the model is ~0.7 but the score varies a lot for different runs. ...
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Graph convolution for multiple disconnected graphs

I have found many examples of graph convolution on TF and StellarGraph that work with a single large graph (like the Cora graph), but I have not found any resources on cases where you have multiple ...
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Any learning method to connect an edgeless graph?

Given N nodes with no edges connecting them at all. Each node has certain n features. Is there a way to connect these nodes and form a connected graph. The idea is to then feed the outputted graph ...
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Can anyone help me how this code extracts features from the graph? [closed]

I have this code from DGCNN Neural Network but i don't understand how it extracts features. In particular i understand that we get the top knn point but i don't understand the idx_base. ...
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1 answer
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Model Suggestion for graph data

I am trying to figure out the right model/algorithm for a graph dataset to develop a machine learning pipeline. I have looked into Graph Neural Network(GNN) but all of the tutorials I found, trained ...
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14 views

How to embed relational information in a Transformer for NMT task?

I have AMR graph like the following: I am using Transformer model for machine translation. However, my input data has relational information as shown above. This information has semantic information ...
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How can I do a time and space complexity comparison of a graph convolutional network vs. a feed forward network / MLP

How can I make a time and space complexity comparison of a graph convolutional network vs a feed-forward network / MLP? Does anybody have an idea how I can compare them?
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1 answer
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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 ...
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How to calculate similarity between the two graphs?

I have to learn similarity between graphs using deep learning. I have many samples (500k) of graphs. How can I compute similarity score between two graphs? I am thinking: convert graphs into vectors ...
2 votes
1 answer
66 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 ...
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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 ...
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How to model a set-to-set mapping with graph neural networks?

I have a task on a heterogeneous graph where a set of nodes is given as input and some of the nodes are acceptable outputs. The dataset essentially consists of pairs (X, Y) where X is a set of nodes ...
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Can GNNs be used to predict the performance of a Neural Network?

Is possible to use a GNN to learn the hyperparameters and structure of a given DNN program (Tensorflow or PyTorch) and predict some metric about the program (accuracy, etc). Apparently, all PyTorch ...
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How to understand the GCN equation?

I understand GCN does message passing with its neighbours to learn the node embedding. But I don't understand the following equation. What "tilda" is referring to equation ...
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71 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 ...
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What is SE(3) transformer and what does RoseTTAFold use it for?

As is mentioned in its paper, the SE(3) transformer is a kind of self-attention-based structure that guarantees SE(3)-equivariance. So what is the reason that RoseTTAFold uses it and what for?
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What are attention heads in a Graph Attention Layers

I am using the EGATConv layer for an edge classification task. One of the constructor's parameters is num_heads, which is number of attention heads. I can't really ...
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In Graph Neural Network is Message Passing Step Agnostic of Output Values during Training?

So Graph Neural Networks is about representation learning where initially representation of graph is learned in the form of node embeddings. My question is: Are the output values back propagated and ...
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Does having more edges on a GNN helps learning?

I am doing a machine translation task using a Graph2Seq graph neutral network. I am using GAT as my encoder. Graph stats: I have around 400 nodes in the graph per data point. In the current graph, on ...
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1 answer
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What is the best GNN for a NMT task?

I am doing a machine translation task using a Graph2Seq graph neutral network. There are many different variants of GNN: GCN GAT GraphSage GGNN Which one would be the most effective for a machine ...
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How can I prove "If the canonical forms of two graphs are not equivalent, then the graphs are definitively not isomorphic." on WL-test?

WL-Test is used for checking whether two graphs are isomorphic or not. It can make a graph to a canonical form. How can I prove that if canonical forms of two graphs are different, then they are non-...
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1 answer
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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 ...
1 vote
1 answer
176 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 ...
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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 ...
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How to model graph node as priority list over a visual scene in neuro-symbolic AI?

Suppose if we have a visual scene graph and we model each component in the scene as a node of a graph and edges which are relationship between the visual scene components. Some of the nodes are like ...
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Temporal Graph Neural Network for motion prediction

Temporal Graph Neural Networks have been used for motion prediction (or traffic forecasting) in the following recent papers: Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion ...
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How can I verify two graphs are isomorphic or not with GNN?

I have read How powerful are Graph Neural Networks, Xu et al. , and I got a question. How can I compare a pair of graphs? I know that graph classification is to classify graphs to some groups. However,...
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Graph Neural Networks: Why do papers use very low label rates?

I was recently reading the following paper: "Semi-supervised classification with Graph Convolutional Networks" by Kipf and Welling (here). Question: When testing on datasets, why are the ...
2 votes
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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 ...
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Why is the Graph Isomorphism Network powerful?

I am reading a paper known as GIN, How powerful are graph neural networks?, Xu et al. 2019 The paper, Lemma 5 and Corollary 6, introduces Graph Isomorphism Network (GIN). In Lemma 5, Moreover, any ...
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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 ...
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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 ...
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1 answer
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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-...
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Why is/can node classification (graph machine learning) be semi-supervised while graph classification is supervised?

I was reading about different graph machine learning tasks in this book (Chapter 1) here and to learn about node classification and graph classification tasks. Then I looked at this paper here, which ...
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1 answer
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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 ...
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Why does conditioning neural network function on adjacency matrix of graph allow for distribution of gradient information from the supervised loss?

I was reading the following paper here and had a question about the paragraph on page 1 (in the introduction). The equation being referred to is: $$ \mathcal{L} = \mathcal{L}_0 + \lambda \mathcal{L}_{\...
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How to initialize the coefficient vector of Deep Tensor Neural Network

In Quantum-Chemical Insights from Deep Tensor Neural Networks, I would like to ask a question about how to initialize the coefficient vector of the network, because I could not understand it even ...
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1 answer
119 views

Does the Weisfeiler-Lehman Isomorphism Test end?

I am studying GNNs. I am interested in the Weisfeiler-Lehman Isomorphism Test (WL-Test). I was looking for information about whether the test always ends or not, but I didn't find a definitive answer. ...
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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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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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25 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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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 $x \in R^N$ (...
1 vote
1 answer
158 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 ...
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2 answers
195 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 ...
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1 answer
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Why does $I_N + D^{-\frac{1}{2}}AD^{-\frac{1}{2}}$ have eigenvalues in the range [0, 2]?

In Semi-supervised classification with Graph Convolutional Networks, I am unable to understand a few things. Given an undirected graph having adjacency matrix $A$, degree matrix $D_{ii} = \sum_j A_{...
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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 ...
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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 ...
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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 ...
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1 answer
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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 ...