<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>

CC BY-SA 4.0
2019-12-04T09:27:52.067Z
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.
razvanc92
CC BY-SA 4.0
2019-12-04T15:50:44.623Z
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.
Newbie1111
<convolutional-neural-networks><geometric-deep-learning>
How should I handle different input sizes in graph convolution networks?
Newbie1111
# 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

