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Questions tagged [graph-neural-network]

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The possible integrations of physics and deep learning?

I have developed a model in my thesis which can compute the energy of a special physical system and some other useful physical quantities. Now I want to use it in deep learning somehow. Do you have ...
Wisdom's user avatar
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spectral gnn forward pass

Following the article: A Practical Guide to Graph Neural Networks: https://arxiv.org/abs/2010.05234 Suppose our graph has $N$ nodes and Laplacian $L = UDU^T$. Let $\Theta$ denote a filter. Let $f_{k}$ ...
André Armatowski's user avatar
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1 answer
25 views

How can a citation dataset (like Cora) have strongly connected components?

This website https://snap.stanford.edu/data/cit-HepPh.html shows that the High-energy physics citation network has strongly connected components and it's driving me crazy. A SCC would mean that you ...
StackExchanger's user avatar
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13 views

graph signal in GNN

I am reading several materials about graph signal processing for a thesis on Graph Neural Network and i see that a graph signal is defined as a vector so each node signal is a scalar. In practice, a ...
endeavor's user avatar
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1 answer
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Asynchronous Training of Deep Learning Models

I am thinking of how would it be if I can create asynchronous forward function in sub-class of nn.Module . When I came across architecture in attached image, I felt that it would be faster if we could ...
Sarvagya_P's user avatar
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graph neural networks for molecular embeddings

I want to extract the embeddings from various graph neural network designed for small molecules. I want to know does the model need to be trained to do the same. Or should be extract the layer in ...
As13's user avatar
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17 views

Systematic bias of neural network regression

I am not sure if here is the correct place to ask this question. I was trying to do graph-level regression task using graph convolutional networks, basically I concatenated 3 linear layers after ...
Tianjian Qin's user avatar
1 vote
1 answer
137 views

What're the standard ways of padding data for GNNs?

I am working on materials property prediction using GNNs with torch_geometric. Each data in my dataset has different number of feature vectors x, edge_index vectors ...
user174967's user avatar
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11 views

What is the prior mu in Heterogeneous Graph Transformer?

I am reading https://arxiv.org/pdf/2003.01332.pdf and do not understand what the prior (\mu) is supposed to be. I also found their implementation on github, but it is still not clear to me. For ...
Servus's user avatar
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171 views

Convert specific domain knowledge text to a knowledge graph

As part of this semester assignment , I'm working on a project that aims to to represent the knowledge in "PMBOK 6th edition, section 11: Project Risk Management (page 395 -> 458)" and the knowledge ...
Wissem Boujlida's user avatar
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how to derive this negative sampling approximation for graphs?

I am watching CS224W video (https://www.youtube.com/watch?v=Xv0wRy66Big&list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn&index=8) and have some issue understanding the following approximation: Does ...
username123's user avatar
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Open question - approach to node classification in a directed multi-relational graph

The problem in hand is: In a strategy game, I need to select characters to form my team, in order to win against a second team, chosen by an opponent player. The universe of characters contains U ...
pedrovgp's user avatar
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Train test split in Graph Convolution Network for image classification

I am trying to construct a GCN for image classification where each pixel is a single node in the graph. However I want to train and test the model within the same graph, so I constructed a single ...
foobar's user avatar
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How to Use Graph Learning Libraries to Predict Edges on a Graph where Each Node Has an Embedding?

An undirectional graph $\mathcal{G}$ has the set of nodes $\mathcal{N}$ where each node has an associated unique embedding of $512$ dimensions. Note that the embeddings themselves are fixed, and not ...
Della's user avatar
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1 answer
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Implementation of Graph Neural Network for Image Classification

I'm currently working on a project where I want to utilize Graph Neural Networks (GNNs) for image classification tasks. However, I'm facing difficulties in understanding how to implement GNNs ...
Rezuana Haque's user avatar
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27 views

Fine-tuning Pretrained Models for Web DOM Interaction Prediction Task

I am currently working on a side project that involves predicting changes to a webpage's DOM based on user interactions. The idea is to input the initial DOM state and a user interaction, and predict ...
DonnyDato's user avatar
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32 views

Imbalanced classes

I am currently dealing with imbalanced classes in my case of binary classification, where one class represents only 4% of the other class. To address this issue, here is the approach I have taken: ...
d3dalo's user avatar
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1 vote
1 answer
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Trouble Training GNN for Binary Node Classification Task

I am using a GNN to solve a problem in which I have a query target and an undirected graph. My goal is to emit a subset of nodes in the graph (via a node-wise binary prediction) whose features sum to ...
mt_'s user avatar
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1 answer
30 views

What are effective ways to merge different parts of a document to form a single document?

I have a dataset where I have 3 different columns (Title, Keyword and Abstract) representing a document. I have to build a text classification model using TextGCN, where documents and words will be ...
ROHAN PADSHAH's user avatar
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1 answer
113 views

How to correctly implement the NNConv in Pytorch?

I tried to make a GNN class which can make use of my node features along with my edge features of the graph.I have implemented NNConv in order to use the edge features, but I am not able to understand ...
Formal_this's user avatar
2 votes
1 answer
233 views

Trying to average node values over local neighborhoods in a graph using a GCN

I'm new to Graph Convolutional Networks (and pytorch in general) so I'm trying to verify that the message passing layer is working as expected before I go on to adding layers to the network. But when ...
dn-ra's user avatar
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1 answer
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Is there a procedure for determining if a classification problem is ill-defined?

Consider a group of objects denoted $O = \{o_0, o_1, \cdots\}$ where each object is associated with a feature vector $F = \{f_0, f_1, \cdots\, f_{N-1}\}$. For this case, assume the features are ...
Ralff's user avatar
  • 123
1 vote
2 answers
78 views

How to cluster components of a graph containing text data?

Suppose that I have a graph that has components like the image below. Graph nodes contain text data (titles) and the edges data is the similarity (percentage). I know that each component represents a ...
Reza Shabrang's user avatar
1 vote
0 answers
23 views

What is purpose of diffusion matrix? What is its role in graph clustering? How do it serve its purpose by equation?

As titled, I am not quite sure what is purpose of Diffusion Matrix, which is support to clustering graph. From this article (https://arxiv.org/pdf/2006.05582v1.pdf) I am might able to compute the ...
Phạm Tâm's user avatar
1 vote
1 answer
385 views

Captum vs GNNExplainer for explainability in Graph Neural Networks

I'm new to Graph Neural Networks and interested in exploring frameworks that allow the identification of nodes/edges that underlie prediction. I came across : (1) a model architecture (GNNExplainer) ...
batlike's user avatar
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1 answer
44 views

Normalizing softmax by dividing by its maximum?

Reading this paper, I'm struggling to understand the step with the question mark (page 3). The formula for $\textbf r$ uses $\textbf q_i$ (no tilde), but the numeric values in the following paragraph ...
Tim's user avatar
  • 103
1 vote
0 answers
80 views

GNN Model - Analyzing Training Curve

Introduction. Actually, I am working on a Graph Neural Network (GNN) model to predict some graph-level float values. So, input=graph, output=float predicted value. I trained and evaluated the proposed ...
Pouya's user avatar
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0 answers
107 views

How to evaluate Light Graph Convolutional Networks (LightGCN) correctly on sparse binary data?

I implemented the LightGCN at work to recommend k items to users according to the TensorFlow implementation of Microsoft: https://github.com/microsoft/recommenders/blob/main/examples/...
mgross's user avatar
  • 109
1 vote
0 answers
16 views

How to convert ECG Data to Graphical Data so that it can be used in GNNs?

I am trying to predict arrythmia using GCNN but the problem i am facing is that the data is in tabular format screenshot attached below. Upon reading i found out that there needs to nodes and edges ...
user135248's user avatar
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1 answer
86 views

Incorporating structural information in a Transformer?

For a Neural Machine Translation (NMT) task, my input data has relational information. This relation could be modelled using a graphical structure. So one approach could be to use Graph Neural Network ...
Exploring's user avatar
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3 votes
4 answers
2k views

How to perform inductive train/test split for GraphSAGE classification

Let's say I have a network that consists of a single weakly connected component. From various papers I've seen that if you want to use inductive GNNs like GraphSAGE, it is advisable to split your ...
Tomaž Bratanič's user avatar
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0 answers
127 views

Why is "Hidden State" at Node in Graph Neural Network Considered "Compressed" Representation?

I am reading article on Graph Neural Network (GNN) and it is mentioned: The memory stores the states of all the nodes, acting as a compressed representation of the node’s past interactions. Why is ...
user0193's user avatar
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1 vote
0 answers
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How to define a graph in GNN? [closed]

I am new to graph neural network (GNN). Without knowing a graph in advance, how can we possibly form an adjacency matrix? Assume there are 3 nodes (vertices): A, B, & C. There are could be many ...
DaCard's user avatar
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1 vote
0 answers
29 views

Are GNNs/GCNs viable for graphs with no node features, with only the unique node IDs? Are they different from DeepWalk at that point?

I started to dig into GNNs for the first time and I have trouble understanding its advantages over NLP inspired embedding methods like DeepWalk and node2vec. Do GNNs only shine with node features? Or ...
oliver.c's user avatar
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1 vote
0 answers
35 views

AI algorithm model that outputs a list of unknown length [closed]

I have a dataset with the following x columns: date time is_weekend is_holiday start_intersection end_intersection The output is a list of intersections, that connect start_intersection with ...
Sharhad Bashar's user avatar
1 vote
0 answers
19 views

Node embeddings and densely connected graphs

I have a dataset of users and their interests (represented by categories) and I’m trying to embed the graph which results from connecting users if they have a common interest, so I’ll add an edge for ...
Adam's user avatar
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1 vote
1 answer
3k views

How train - test split works for Graph Neural Networks

I have recently started studying GNN's. I have covered GCN and GraphSage so far. But I am confused regarding the process when testing occurs. Now suppose in the graph above I am using the nodes as ...
Sourajit's user avatar
  • 113
1 vote
1 answer
49 views

How to define similarity between nodes in original graph?

While there has been a lot of talk in how to define the similarity between nodes in the embedding space, but I don't seem to come across any talking about defining the similarity between nodes in the ...
Student's user avatar
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1 vote
0 answers
57 views

How to create a graph network (Networkx) from the solution of Ordinary differential equations?

I have a list of Nodes. Suppose, N =["1","2","3","4"] # there can be different number of nodes And I have solved the ...
Atom Store's user avatar
1 vote
2 answers
726 views

Graph Neural Network fails at generalizing on unseen graph topologies

I'm using PytorchGeometric to train a graph convolutional network for regression over nodes problem (the graph models physical phenomena in the network of sensors; the network of sensors is actually ...
sesli's user avatar
  • 11
1 vote
0 answers
14 views

How to construct a graph using Neural Structured Learning framework?

My dataset (both features and label) consists of continuous variables. Dimension of features is (12,). The number of samples are of 7th order of magnitude (about 11 ...
KostasVlachos's user avatar
1 vote
0 answers
177 views

Applying network diffusion to a graph created with the igraph library in R

I have created a graph using the igraph library. ...
Leonel Parrales's user avatar
0 votes
0 answers
91 views

Validation accuracy does not increase for binary classification using GNN

I am trying to perform graph classification with GINConv model of GNN. I have tried everything from varying dropouts to weight decay (for L2 regularization), learning rate (1e-6 to 1e-3), batch ...
user3598542's user avatar
-1 votes
2 answers
37 views

Optimisation of neural networks

Do neural networks get optimized by trial and error, by data scientists, or is there some way of optimizing values through accurate mathematical equations?
Domenico Bagnato's user avatar
4 votes
1 answer
7k views

What are latent representations?

I am reading some research papers about graph convolutional neural networks and I have seen the term "latent representations" used a lot. For instance, "the model was able to learn ...
user2362790's user avatar
0 votes
1 answer
94 views

Understanding Node Embeddings

I have only just started to look into graph neural networks and I am a little confused on the node embedding process. Here is my understanding, please let me know if i misunderstood: Given unlabelled ...
CCZ23's user avatar
  • 111
0 votes
2 answers
39 views

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): ...
An Ignorant Wanderer's user avatar
1 vote
0 answers
166 views

Dying gradient issue in Graph Neural Networks

I am using Pytorch-Geometric library to implement a Graph Convolutional Layer(GCN) followed by few linear layers for a prediction task. But after training on graphs with np. of nodes being 10K and no. ...
Kooled's user avatar
  • 11
1 vote
1 answer
155 views

What is a good approach for embedding both textual and spatial features for document classification?

I am working on a document classifier that can perform the classification based on the document structure as well. My plan is to get the word embedding as well as the word coordinates and somehow ...
Akas Antony's user avatar
1 vote
0 answers
35 views

(Graph Convolutional Network (GCN) based recommender system maintenance issue [closed]

I have built an item-item recommender model using (Graph Convolutional Network (GCN) for an E-commerce website. Could you please help me with the maintenance of the model. How often should I retrain ...
Avinash's user avatar
  • 21