Answers.Body: <h2>Graph Neural Networks</h2>
  <p>The term <em>Graph Neural Network</em>, in its broadest sense, refers to any Neural Network designed to take graph structured data as its input:</p>
  <blockquote>
  <p>To cover a broader range of methods, this survey considers GNNs as all deep learning approaches for graph data.</p>
  </blockquote>
  <ul>
  <li><a href="https://arxiv.org/abs/1901.00596" rel="nofollow noreferrer">A Comprehensive Survey on Graph Neural Networks</a>, Wu et al (2019)</li>
  </ul>
  <p>However the original paper to propose the term specifically referred to recursive neural networks adapted to take graph-structured data as their input:</p>
  <blockquote>
  <p>This paper presents a new neural model, called graph neural network (GNN), capable of directly processing graphs. GNNs extends recursive neural networks and can be applied on most of the practically useful kinds of graphs, including directed, undirected, labelled and cyclic graphs.</p>
  </blockquote>
  <ul>
  <li><a href="https://ieeexplore.ieee.org/document/1555942" rel="nofollow noreferrer">A new model for learning in graph domains</a> (2005)</li>
  </ul>
  <h2>Subtypes</h2>
  <p>Note, Wu et al propose a taxonomy dividing GNN's into four subgroups:</p>
  <ul>
  <li>Recurrent graph neural networks (RecGNN)</li>
  <li>Convolutional graph neural networks (ConvGNN)</li>
  <li>Graph autoencoders (GAE)</li>
  <li>Spatial-temporal graph neural networks (STGNN)</li>
  </ul>
  <p>ConvGNN's can themselves be classified by whether they use Spectral methods or Spatial methods, and GAE's by whether they are designed for Network embedding or Graph generation.</p>
  
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Answers.CreationDate: 2020-04-25T18:51:41.680Z
Answers.Text: Of course, after all this time, I am already familiar with GNNs. However, I think that one of the unclearest things when it comes to GNNs is how they are trained. Are they still trained with GD? What are the labels? What are the loss functions? You could talk a little bit about that (then I would accept your answer).
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Body: <p>What is a graph neural network (GNN)?</p>
  <p>Here are some sub-questions</p>
  <ul>
  <li>How is a GNN different from a NN?</li>
  <li>How exactly is a GNN related to graphs?</li>
  <li>What are the components of a GNN? What are the inputs and outputs of GNNs?</li>
  <li>How can GNNs be trained? Can we also use gradient descent with back-propagation to train GNNs?</li>
  </ul>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-03-14T10:34:49.513Z
Comments.Text: If someone is interested in answering to this question, have a look at this article [A Gentle Introduction to Graph Neural Networks (Basics, DeepWalk, and GraphSage)](https://towardsdatascience.com/a-gentle-introduction-to-graph-neural-network-basics-deepwalk-and-graphsage-db5d540d50b3) and the research article [A New Model for Learning in Graph Domains](https://www.researchgate.net/profile/Franco_Scarselli/publication/4202380_A_new_model_for_earning_in_raph_domains/links/0c9605188cd580504f000000/A-new-model-for-earning-in-raph-domains.pdf) (which the paper that introduced GNN).
Comments.UserDisplayName: nbro
Tags: <deep-learning><definitions><geometric-deep-learning><graph-neural-networks>
Title: What is a graph neural network?
UserDisplayName: nbro
ThreadText: # What is a graph neural network?
  
  (Asked by: username_0 on 2019-03-12)
  
  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 outputs of GNNs?
  * How can GNNs be trained? Can we also use gradient descent with back-propagation to train GNNs?
  
  **Tags:** deep-learning, definitions, geometric-deep-learning, graph-neural-networks
  
  > **Comments:**
  > **username_0**: If someone is interested in answering to this question, have a look at this article [A Gentle Introduction to Graph Neural Networks (Basics, DeepWalk, and GraphSage)](https://towardsdatascience.com/a-gentle-introduction-to-graph-neural-network-basics-deepwalk-and-graphsage-db5d540d50b3) and the research article [A New Model for Learning in Graph Domains](https://www.researchgate.net/profile/Franco_Scarselli/publication/4202380_A_new_model_for_earning_in_raph_domains/links/0c9605188cd580504f000000/A-new-model-for-earning-in-raph-domains.pdf) (which the paper that introduced GNN).  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-04-25. Score: 2
  
  ## Graph Neural Networks
  
  The term *Graph Neural Network*, in its broadest sense, refers to any Neural Network designed to take graph structured data as its input:
  
  > To cover a broader range of methods, this survey considers GNNs as all deep learning approaches for graph data.
  
  * [A Comprehensive Survey on Graph Neural Networks](https://arxiv.org/abs/1901.00596), Wu et al (2019)
  
  However the original paper to propose the term specifically referred to recursive neural networks adapted to take graph-structured data as their input:
  
  > This paper presents a new neural model, called graph neural network (GNN), capable of directly processing graphs. GNNs extends recursive neural networks and can be applied on most of the practically useful kinds of graphs, including directed, undirected, labelled and cyclic graphs.
  
  * [A new model for learning in graph domains](https://ieeexplore.ieee.org/document/1555942) (2005)
  
  ## Subtypes
  
  Note, Wu et al propose a taxonomy dividing GNN's into four subgroups:
  
  * Recurrent graph neural networks (RecGNN)
  * Convolutional graph neural networks (ConvGNN)
  * Graph autoencoders (GAE)
  * Spatial-temporal graph neural networks (STGNN)
  
  ConvGNN's can themselves be classified by whether they use Spectral methods or Spatial methods, and GAE's by whether they are designed for Network embedding or Graph generation.
  
  > **Comments:**
  > **username_0**: Of course, after all this time, I am already familiar with GNNs. However, I think that one of the unclearest things when it comes to GNNs is how they are trained. Are they still trained with GD? What are the labels? What are the loss functions? You could talk a little bit about that (then I would accept your answer).  
  
  ---
  
  