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 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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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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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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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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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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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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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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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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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 ...
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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 ...
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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 ...
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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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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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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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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 ...
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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 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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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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42
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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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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 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 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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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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ST-GCN: graph convolution operator in Geometry-Aware Interaction Network (GAIN)
I need help implementing the model in this paper:
They have adopted spatio-temporal graph convolution operator in ST-GCN [section 3.1.2]. I've found there is popular libraries available for GCN: ...
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18
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Link Prediction Task on Multigraphs with GraphSAGE
I need to perform a link prediction task on a heteronegous multigraph (multi-node types, multi-edge types, multi-edges between pairs of nodes, node features and edge features) in the inductive setting....
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Requesting resources on causal networks for 2D strategy game
I am requesting research, articles, abstracts or interesting opinions that will help me create a complex causal neural network. There are many detailed resources on causal discovery, image recognition,...
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25
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Several kinds of edges in a GNN
I have an existing implementation (written by somebody else) of an MPNN using the graph_nets library. The graph net is based on a tree, but has 4 times as many edges: if U is the parent of V and R is ...
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How can one incorporate spatial correlations into time series forecasting?
I am working on a project, where I am trying to predict temperatures of various streets and I have their locations recorded.
I was wondering if I could somehow train a model that could incorporate ...
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1
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Do GNNs operate on enitre graphs or do they basically iterate over each node one-by-one?
I understand how GNNs/GCNs aggregate an arbitrary number of nodes' information from the neighborhood of a target node in order to predict an attribute of that target node. What I don't understand is, ...
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Is there a test to determine if a feature space is sufficient for a classification problem?
I am working with GNNs for a node classification problem, and I am only able to achieve about 50% accuracy for the training set. I am not able to overfit the model to the training data. This makes me ...
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61
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Understanding relation between parameter sharing and Message Passing in Graph Neural Networks
Ravanbakhsh has clearly stated the relation between equivariance and parameter-sharing in neural networks. What I'm missing though, is where (and how) this relation becomes clear by considering the ...
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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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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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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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202
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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 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==...
