Questions tagged [graph-neural-networks]
For questions related to graph neural networks, which are artificial neural networks applied to graphs.
83
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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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What is GNN Cheatsheet in PyG Docs
I am going through the PytorchGeometry documentation: https://pytorch-geometric.readthedocs.io/en/latest/index.html which is built on Pytorch .Here they mentioned about GNN Cheatsheet: https://pytorch-...
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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 ...
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Which models can be applied recursively?
I come from a math background, so I am not up-to-date with machine learning literature.
For the purpose of learning dynamics, I would like to train a model to minimize the following loss:
$$\mathcal{L}...
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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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Why is the output of my graph neural network not permutation equivariant?
I am using Pytorch to train a graph neural network on a 4x4 graph. Each node has one feature, and the output has one feature. Essentially, the architecture of my GNN looks like this (I'm training the ...
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103
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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 ...
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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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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 ...
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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$, ...
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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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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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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
...
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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 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 ...
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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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How to reduce variance in F1 scores of GAT across multiple runs while using PU Loss?
I am training GAT using a custom loss function(PU Loss) on the Cora and Citeseer dataset. My training file looks like
...
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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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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 ...
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555
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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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Resources and papers about Graph Neural Networks and molecular predictions?
What are some famous papers or techniques related to the use of Graph Neural Networks (GNNs) for predicting molecular properties?
For example, I know of a common convolutional layer that has obtained ...
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Is it possible to perform node-level classification and graph-level classification on the same graph? [closed]
I would like to classify the nodes of each graph in a multigraph, and transform the graph structure (or delete some of the nodes).
And then I want to do a graph-level classification problem.
Are there ...
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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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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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125
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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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366
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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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144
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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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398
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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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187
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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 ...
- 253
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288
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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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142
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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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1
answer
161
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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 ...
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536
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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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572
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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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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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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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216
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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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287
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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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950
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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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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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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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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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202
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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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1
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731
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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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60
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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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190
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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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