Answers.Body: <p>There are some problems that involve graphs and manifolds (sometimes collectively called <em>non-Euclidean</em> data), such as <a href="https://arxiv.org/pdf/1802.04364.pdf" rel="nofollow noreferrer">molecule design and generation</a>, <a href="https://academic.oup.com/bioinformatics/article/34/13/i457/5045770" rel="nofollow noreferrer">drug repositioning</a>, social networks analysis, <a href="https://www.ncbi.nlm.nih.gov/pubmed/28412442" rel="nofollow noreferrer">brain imaging</a>, <a href="https://arxiv.org/pdf/1902.06673.pdf" rel="nofollow noreferrer">fake news detection</a>, <a href="https://arxiv.org/pdf/1704.06803.pdf" rel="nofollow noreferrer">recommender systems</a>, <a href="https://arxiv.org/pdf/1809.06166.pdf" rel="nofollow noreferrer">neutrino detection</a>, computer vision and graphics and <a href="https://arxiv.org/pdf/1712.00268v1.pdf" rel="nofollow noreferrer">shape (e.g. hand or face) completion</a> or generation (generative models).</p>
  
  <p>The main benefit of <a href="https://arxiv.org/pdf/1611.08097.pdf" rel="nofollow noreferrer"><em>geometric deep learning</em></a> (deep learning applied to graphs and manifolds) is that you do not lose the information encoded in the graphs (or manifolds), which, otherwise, you would likely lose because you would need to convert your graphs (or manifolds) to an equivalent vector representation that you can feed into the existing CNN or other standard neural networks.</p>
  
  <p>Note that you cannot directly apply the usual convolution operation to graphs, because, for example, graphs do not have the notion of relative positions of the nodes. Furthermore, note that <em>graph networks</em> have little to do with CNNs, even if they are sometimes called <em>graph convolution networks</em>.</p>
  
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Answers.CreationDate: 2019-06-26T18:10:38.403Z
Answers.LastActivityDate: 2019-06-26T18:29:58.930Z
Answers.LastEditDate: 2019-06-26T18:29:58.930Z
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Body: <p>I'm new to the graph convolution network. I wonder what is the main purpose of applying data with graph structure to CNN?</p>
  
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Comments.CreationDate: 2020-04-26T09:08:00.130Z
Comments.Text: Does this answer your question? [What benefits can be got by applying Graph Convolutional Neural Network instead of ordinary CNN?](https://ai.stackexchange.com/questions/7788/what-benefits-can-be-got-by-applying-graph-convolutional-neural-network-instead)
Comments.UserDisplayName: brazofuerte
Tags: <machine-learning><deep-learning><convolutional-neural-networks><geometric-deep-learning>
Title: What is the purpose and benefit of applying CNN to a graph?
UserDisplayName: unsmoother
ThreadText: # What is the purpose and benefit of applying CNN to a graph?
  
  (Asked by: username_0 on 2019-06-26)
  
  I'm new to the graph convolution network. I wonder what is the main purpose of applying data with graph structure to CNN?
  
  **Tags:** machine-learning, deep-learning, convolutional-neural-networks, geometric-deep-learning
  
  > **Comments:**
  > **username_1**: Does this answer your question? [What benefits can be got by applying Graph Convolutional Neural Network instead of ordinary CNN?](https://ai.stackexchange.com/questions/7788/what-benefits-can-be-got-by-applying-graph-convolutional-neural-network-instead)  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2019-06-26. Score: 2 | Accepted answer
  
  There are some problems that involve graphs and manifolds (sometimes collectively called *non-Euclidean* data), such as [molecule design and generation](https://arxiv.org/pdf/1802.04364.pdf), [drug repositioning](https://academic.oup.com/bioinformatics/article/34/13/i457/5045770), social networks analysis, [brain imaging](https://www.ncbi.nlm.nih.gov/pubmed/28412442), [fake news detection](https://arxiv.org/pdf/1902.06673.pdf), [recommender systems](https://arxiv.org/pdf/1704.06803.pdf), [neutrino detection](https://arxiv.org/pdf/1809.06166.pdf), computer vision and graphics and [shape (e.g. hand or face) completion](https://arxiv.org/pdf/1712.00268v1.pdf) or generation (generative models).
  
  The main benefit of [*geometric deep learning*](https://arxiv.org/pdf/1611.08097.pdf) (deep learning applied to graphs and manifolds) is that you do not lose the information encoded in the graphs (or manifolds), which, otherwise, you would likely lose because you would need to convert your graphs (or manifolds) to an equivalent vector representation that you can feed into the existing CNN or other standard neural networks.
  
  Note that you cannot directly apply the usual convolution operation to graphs, because, for example, graphs do not have the notion of relative positions of the nodes. Furthermore, note that *graph networks* have little to do with CNNs, even if they are sometimes called *graph convolution networks*.
  
  
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