Questions tagged [graph-neural-network]

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knowledge graph embeddings using GNN vs. shallow embeddings

I want to encode a knowledge graph, but I'm unclear on the difference between using shallow encodings (TransE, RotatE, etc.) vs a GNN such as a GraphSAGE. It seems like the GNN approach would also ...
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Cause of randomness in AUC score for GNN

I have implemented a GraphSAGE model using dgl for link prediction. On average the auc score of the model is ~0.7 but the score varies a lot for different runs. ...
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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) ...
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Regression task using graph neural networks

I consider the following scenario: we have a weighted undirected graph where each node has several features. I want to predict a value for each node of the graph that such that a global objective ...
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Is it possible to apply graph neural networks on a sequence to determine a hidden states of its elements (instead of HMM)?

For example, I have a sequence of observations from {$y_1$, ..., $y_{4}$}: $y_1, y_2, y_1, y_3, y_2, y_2, y_2, y_4 $ And the sequence is produced by a hidden states from {$X1$, $X2$, $X3$}. Usually ...
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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 ...
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What is effect of having more edges on a GCN helps learning?

I am using a Graph2Seq GNN. For my case, I am using GCN as my encoder. For my graph, on the average: I have around 500 nodes in the graph per data point. In the current graph, on average, a node is ...
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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 ...
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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/...
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In Graph neural network - SAGE why do we normalize with l2 norm in after a k-hop iteration?

Hello I am trying to undestand the message propagation algorithm of GraphSAGE(https://arxiv.org/pdf/1706.02216.pdf) In step 7 there is a division with l2 norm (if I understand the notation correctly )....
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What is meant by softly selecting a matrices from a set of matrices?

How is the softmax layer applied to the 1x1 conv layer in order to soflty select the matrix from a set of matrix denoted by A. From my understanding of convolutions, implementing a 1x1 conv layer on a ...
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Graph Neural Networks for Segmented Images - Which Nodes do I connect?

I'm facing an interesting problem involving medical images. We are set out to test an hypothesis if certain objects in an image affect the diagnosis of a patient. I would love to hear any comments ...
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Model stalls and learning slows down after the first epochs

I have an issue with a model that I'm working on, I cannot show you the model architecture because it's basically confidential research. The model includes Graph convolutional networks and Transformer ...
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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 ...
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Can I use low dimensional node features in graph convolutional networks?

I am trying to understand how GCNs work. For example, the well known GraphSAGE algorithm considers a graph $G$ with node features $x_i$ of dimension $n$. Then it propagates the node features over the ...
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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 ...
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359 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 ...
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Facing problem on X-axis and Y-axis in VS Code

Helow everyone. I am facing a problem on x axis and y axis. when i got the output then the output plot look like as,, The problem is output plot do not show the x-axis label and y axis label. Please ...
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How to find union of nodes in rdflib knowledge graph

Little background on Work : I am working with ontologies and for my usecase I have to apply random walk on the ontology nodes/entities. In order to do the same I have written one function - that given ...
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How to correct a validation loss in a regression problem?

I've developed a graph neural network using PyTorch Geometric. My model looks like: ...
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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 ...
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Graph Classification via Random Forest

This is my first post here, just a brief presentation: my name is Gianmarco, I’m Medicinal Chemistry undergraduate student who is preparing his dissertation, my idea would be to create a classifier ...
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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 ...
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In the node2vec model derivation, what does it mean for node representations to be "Symmetric in Feature Space"?

The main derivation of the probabilistic model in Node2Vec goes as follows (paper available on ArXiv: https://arxiv.org/pdf/1607.00653.pdf): We formulate feature learning in networks as a maximum ...
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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 ...
1 vote
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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 ...
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Graph convolution with global information

Is it possible to add global information in addition to node information using Spektral or Pythorch? For instance, information of nodes such as relative position and atomic mass give a molecular ...
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51 views

Multivariate time series data forecasting using Graph Neural networks

I am using the article "Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks" code "https://github.com/nnzhan/MTGNN" to train my data sets having ...
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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 ...
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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 ...
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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 ...
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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 ...
1 vote
2 answers
244 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 ...
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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 ...
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59 views

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

I have created a graph using the igraph library. ...
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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 ...
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2 answers
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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?
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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 ...
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1 answer
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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 ...
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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): ...
1 vote
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87 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. ...
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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 ...
1 vote
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32 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 ...
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Unstable Training when combining Graph Neural Networks for Graph Classification Tasks

I have been combining Graph Convolutional Layers and Graph Pooling layers to define a neural network architecture for Graph Classification tasks. Specifically, using the Graph Convolutional Layer ...
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Machine learning on graphs

I'm looking for some method/model to help me with my current problem: I have a geometry, consisting of points, and eges. For each point I take information about itself and its neighbours. For now I ...
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Force Matching in Coarse Grained Molecular Dynamics with Jax - Forces do not match when neglecting energy loss

I am currently exploring force matching approaches for molecular dynamic simulations. As I am still in an exploration state, I'd tried investigated Force Matching Neural Network Colab Notebook ...
1 vote
1 answer
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From Labels to Graph: what machine learning approaches to use?

Imagine a world where we have places (e.g., cities, restaurants, national parks, etc.) but no roads connecting them. Our objective is to build roads connecting any two places while going through some ...
1 vote
1 answer
517 views

What are the differences between Knowledge Graph Embeddings (KGE) and Graph Neural Network (GNN)

From page 3 of this paper Knowledge Graph Embeddings and Explainable AI, they mentioned as below: Note that knowledge graph embeddings are different from Graph Neural Networks (GNNs). KG embedding ...
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1 answer
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Knowledge Graph as an input to a neural network

I want to create a neural network that takes as an input a knolwedge subgraph(different types of nodes and different types of edges) to predict some properties. For instance an input in the graph can ...
3 votes
2 answers
121 views

Isn't graph embedding a step back from non-euclidean space?

As I understand, we use graph embedding to make a euclidean representation of non-euclidean structure - graph. Does it mean that conceptually we just take a step back to, may be, more complex, but ...
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