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.
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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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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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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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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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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 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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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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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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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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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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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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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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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 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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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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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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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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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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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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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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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 ...
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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 ...
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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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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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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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1
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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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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 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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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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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 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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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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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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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 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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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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