Body: <p>I'm a student beginning to study deep learning, and would like to practice with a simple project using a Graph Convolutional Network.</p>
  
  <p>However, I'm not quite sure how to handle different input sizes of graphs for the GCN. How would I do this?</p>
  
  <p>Is zero-padding the only way to solve this problem? While zero-padding is applicable to CNNs, I'm not sure if it is for a GCN.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-12-04T09:27:52.067Z
Comments.Text: Hello and welcome. Can you provide some details about the problem you're trying to solve? I might be able to point you to some relevant literature.
Comments.UserDisplayName: razvanc92
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-12-04T15:50:44.623Z
Comments.Text: Sorry to late reply. I hope to use GCN to study protein structure and its relevant property. As you know different protein has different length of sequence, hence different size of graph.
Comments.UserDisplayName: Newbie1111
Tags: <convolutional-neural-networks><geometric-deep-learning>
Title: How should I handle different input sizes in graph convolution networks?
UserDisplayName: Newbie1111
ThreadText: # How should I handle different input sizes in graph convolution networks?
  
  (Asked by: username_0 on 2019-12-04)
  
  I'm a student beginning to study deep learning, and would like to practice with a simple project using a Graph Convolutional Network.
  
  However, I'm not quite sure how to handle different input sizes of graphs for the GCN. How would I do this?
  
  Is zero-padding the only way to solve this problem? While zero-padding is applicable to CNNs, I'm not sure if it is for a GCN.
  
  **Tags:** convolutional-neural-networks, geometric-deep-learning
  > **Comments:**
  > **username_1**: Hello and welcome. Can you provide some details about the problem you're trying to solve? I might be able to point you to some relevant literature.  
  > **username_0**: Sorry to late reply. I hope to use GCN to study protein structure and its relevant property. As you know different protein has different length of sequence, hence different size of graph.  
  
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  ## 0 answers
  
  