Answers.Body: <p>Bioinformatics is an area that Graph Convolutional Neural Network is useful. Consider protein networks, or gene-gene networks. Surely, the biological networks can be represented as a graph. Now, you should see how GCN is useful for bioinformatics.</p>
  
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Answers.Body: <p>Generally speaking a graph CNN is applied to data represented by <a href="https://en.wikipedia.org/wiki/Graph_(abstract_data_type)" rel="nofollow noreferrer">graphs</a>, not images. </p>
  
  <ul>
  <li><p>a <strong>graph</strong> is a collection of nodes and edges connecting them.</p></li>
  <li><p>an <strong>image</strong> is a 2D or 3D matrix, in which each element denotes a pixel in space</p></li>
  </ul>
  
  <p>If your data are just images, or something similar (e.g. some fMRI data), you usually cannot benefit from graph CNN compared with usual CNN.</p>
  
  <p>Sometimes, the class labels of your images may be organized in a graph-like  (or tree-like) structure. In that case, you may have a chance to benefit from graph CNN.</p>
  
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Body: <p>What benefits can we got by applying Graph Convolutional Neural Network instead of ordinary CNN? I mean if we can solve a problem by CNN, what is the reason should we convert to Graph Convolutional Neural Network to solve it? Are there any examples i.e. papers can show by replacing ordinary CNN with Graph Convolutional Neural Network, an accuracy increasement or a quality improvement or a performance gain is achieved? Can anyone introduce some examples as image classification, image recognition especially in medical imaging, bioinfomatics or biomedical areas?</p>
  
Tags: <machine-learning><deep-learning><convolutional-neural-networks><graphs><geometric-deep-learning>
Title: What benefits can be got by applying Graph Convolutional Neural Network instead of ordinary CNN?
UserDisplayName: piratesailor
ThreadText: # What benefits can be got by applying Graph Convolutional Neural Network instead of ordinary CNN?
  
  (Asked by: username_0 on 2018-09-02)
  
  What benefits can we got by applying Graph Convolutional Neural Network instead of ordinary CNN? I mean if we can solve a problem by CNN, what is the reason should we convert to Graph Convolutional Neural Network to solve it? Are there any examples i.e. papers can show by replacing ordinary CNN with Graph Convolutional Neural Network, an accuracy increasement or a quality improvement or a performance gain is achieved? Can anyone introduce some examples as image classification, image recognition especially in medical imaging, bioinfomatics or biomedical areas?
  
  **Tags:** machine-learning, deep-learning, convolutional-neural-networks, graphs, geometric-deep-learning
  
  
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  ## 2 answers
  
  ### Answer by username_2 on 2018-09-02. Score: 3 | Accepted answer
  
  Generally speaking a graph CNN is applied to data represented by [graphs](https://en.wikipedia.org/wiki/Graph_(abstract_data_type)), not images.
  
  * a **graph** is a collection of nodes and edges connecting them.
  * an **image** is a 2D or 3D matrix, in which each element denotes a pixel in space
  
  If your data are just images, or something similar (e.g. some fMRI data), you usually cannot benefit from graph CNN compared with usual CNN.
  
  Sometimes, the class labels of your images may be organized in a graph-like (or tree-like) structure. In that case, you may have a chance to benefit from graph CNN.
  
  
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  ### Answer by username_1 on 2018-09-03. Score: 2
  
  Bioinformatics is an area that Graph Convolutional Neural Network is useful. Consider protein networks, or gene-gene networks. Surely, the biological networks can be represented as a graph. Now, you should see how GCN is useful for bioinformatics.
  
  
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