Questions tagged [graph-neural-network]

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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 ...
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21 views

From Labels to Graph: what machine learning approaches to use?

I want to drive from Seattle to San Francisco, and I've some time for sightseeing: what route should I choose to eat nice food and on my way, see some beautiful sides? I would model the world as a ...
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23 views

HinSAGE link existence prediction?

From the demo of HinSAGE link prediction I’ve understood that the model can predict the label of a link between two nodes. I’m looking to know if a link should be existing (binary). For example, let’s ...
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10 views

What is the contraction map constraint in the context of Graph Neural Nets?

In this paper https://arxiv.org/pdf/1511.05493.pdf (GATED GRAPH SEQUENCE NEURAL NETWORKS,2016), it is stated that in a Graph Neural network initialising hidden states is not required, as 'In GNNs, ...
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7 views

Term Weighting on Graph Convolutional Networks

i have read paper about graph convolutional networks https://arxiv.org/abs/1609.02907 since it a document classification task, is it important to choose term weigthing method like tf-idf. the default ...
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35 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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12 views

Multiple sizes and types of Inputs for a NN

I have multiple inputs for a Neural Network and a regression problem. The first input is a Adjacency matrix, the two other are one hot encodings(or binary). I am using the Flatten layer for each of ...
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8 views

Is there any inductive Graph Variational Auto Encoder?

I have been reading about how we can model a Variational AutoEncoder (VAE) into a Graph Variational AutoEncoder (GVAE) where the decoder reconstructs the adjacency matrix. I presume that this approach ...
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1answer
38 views

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 ...
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18 views

Graph Neural Network - Node and Global Properties

I am trying to work with Graph neural networks. I am trying to use the 3d coordinates and some othe things related to those positions as features. However the whole system also has some global ...
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53 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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65 views

Tensorflow dtype=resource

I have created a tensor: ...
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1answer
42 views

How does graph classification work with graph neural networks

I am reading the paper The Graph Neural Network Model by Scarselli et al. I understand how node classification works. I am having trouble understanding how graph classification works however. In ...
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5 views

Deep Graph tutorial, visualization not displaying

I am following this Deep Graph tutorial to learn more about GNNs. In the last step, they create a visualization of the network training, using the following code: ...
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1answer
77 views

What is difference between transductive and inductive in GNN?

It seems in GNN(graph neural network), in transductive situation, we input the whole graph and we mask the label of valid data and predict the label for the valid data. But is seems in inductive ...