Questions tagged [graph-neural-networks]

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

Filter by
Sorted by
Tagged with
0 votes
0 answers
7 views

How to perform inference on a new node using GraphSage

I'm working with the GraphSage architecture to compute node embeddings right now. I understand that during training you fine tune the models parameters and then once fine tuned you can run this on a ...
Kiran Manicka's user avatar
0 votes
0 answers
35 views

How to classify a new node into an existing cluster of nodes?

I have a graph with many disjoint subgraphs that are not connected to each other. Essentially these subgraphs could represent different clusters. What is a general process to figure out node ...
Kiran Manicka's user avatar
0 votes
0 answers
10 views

Using computer vision to comprehend Piping and Instrumentation Diagrams

I'm wondering how to approach this problem. I want to create an excel document (or just a dictionary) where each instrument (circle objects FV 1031, for example) is associated with a line number (for ...
Daniel Caoili's user avatar
0 votes
0 answers
68 views

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: ...
Kholdarbekov's user avatar
0 votes
0 answers
34 views

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....
Jason's user avatar
  • 1
1 vote
2 answers
81 views

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 ...
Vardaan Pahuja's user avatar
1 vote
1 answer
142 views

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 ...
oliver.c's user avatar
0 votes
0 answers
16 views

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,...
Mitsuformation's user avatar
-1 votes
1 answer
122 views

What is GNN Cheatsheet in PyG Docs [closed]

I am going through the Pytorch Geometric documentation: https://pytorch-geometric.readthedocs.io/en/latest/index.html which is built on Pytorch .Here they mentioned about GNN Cheatsheet: https://...
sripathi akhil's user avatar
3 votes
1 answer
82 views

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 ...
MomentumEigenstate's user avatar
0 votes
1 answer
76 views

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}...
user572780's user avatar
0 votes
0 answers
49 views

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 ...
Lev's user avatar
  • 101
0 votes
1 answer
160 views

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 ...
Acad's user avatar
  • 111
2 votes
1 answer
146 views

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 ...
RobJan's user avatar
  • 123
0 votes
0 answers
12 views

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 ...
user380572's user avatar
1 vote
1 answer
35 views

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 ...
GH HONG's user avatar
  • 13
2 votes
1 answer
119 views

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 $U_t$, ...
dmasny's user avatar
  • 23
0 votes
1 answer
44 views

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, ...
oliver.c's user avatar
1 vote
0 answers
185 views

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 ...
Angelo's user avatar
  • 211
4 votes
1 answer
286 views

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 ...
Rexcirus's user avatar
  • 1,154
1 vote
0 answers
24 views

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 ...
Hadar Shavit's user avatar
1 vote
1 answer
139 views

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 ...
puradrogasincortar's user avatar
0 votes
1 answer
33 views

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 ...
willtryagain's user avatar
1 vote
1 answer
135 views

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 ...
han's user avatar
  • 11
1 vote
1 answer
755 views

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 ...
Achiles Br's user avatar
0 votes
1 answer
33 views

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 ...
FineFynmen's user avatar
-1 votes
1 answer
58 views

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. ...
jjnounde's user avatar
0 votes
1 answer
139 views

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 ...
Masudul Hasan Masud's user avatar
1 vote
1 answer
419 views

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 ...
Protostome's user avatar
2 votes
1 answer
190 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 ...
xuq01's user avatar
  • 23
2 votes
1 answer
497 views

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 ...
Prince Bhatti's user avatar
0 votes
1 answer
197 views

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 ...
Exploring's user avatar
  • 303
1 vote
1 answer
347 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 ...
Imago's user avatar
  • 111
1 vote
0 answers
26 views

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 ...
user0193's user avatar
  • 145
0 votes
1 answer
162 views

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 ...
Exploring's user avatar
  • 303
1 vote
1 answer
193 views

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 ...
Exploring's user avatar
  • 303
1 vote
1 answer
592 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 ...
selin's user avatar
  • 11
1 vote
1 answer
627 views

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 ...
user0193's user avatar
  • 145
1 vote
0 answers
29 views

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 ...
bigboss's user avatar
  • 31
1 vote
0 answers
83 views

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 ...
BraveDistribution's user avatar
1 vote
0 answers
87 views

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 ...
Rocky the Owl's user avatar
2 votes
0 answers
144 views

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 ...
desert_ranger's user avatar
1 vote
0 answers
255 views

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 ...
JAEMTO's user avatar
  • 125
1 vote
1 answer
308 views

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 ...
JAEMTO's user avatar
  • 125
1 vote
1 answer
1k views

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 ...
willtryagain's user avatar
1 vote
1 answer
113 views

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-...
Rocky the Owl's user avatar
3 votes
1 answer
2k views

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 ...
tangolin's user avatar
  • 153
0 votes
0 answers
17 views

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 ...
musako's user avatar
  • 181
0 votes
1 answer
220 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. ...
JAEMTO's user avatar
  • 125
1 vote
0 answers
29 views

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 ...
Rocky the Owl's user avatar