Questions tagged [graph-neural-networks]
For questions related to graph neural networks, which are artificial neural networks applied to graphs.
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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$ (...
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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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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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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 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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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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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 ...
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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 ...
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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 ...
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39
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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==...
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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 ...
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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 ...
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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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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 ...
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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 ...
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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 ...
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Can I think of the graph convolution operation as a regular 2D convolution for 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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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 ...
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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 ...
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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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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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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 ...
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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 ...
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What are some conferences for publishing papers on graph convolutional networks?
What are some conferences for publishing papers on graph convolutional networks?
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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 ...
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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-...
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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 ...
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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 ...
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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 ...
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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.
...
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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 ...
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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?
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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 ...
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