Body: <p>I was recently reading the following paper: &quot;Semi-supervised classification with Graph Convolutional Networks&quot; by Kipf and Welling (<a href="https://arxiv.org/pdf/1609.02907.pdf" rel="nofollow noreferrer">here</a>).</p>
  <p><strong>Question</strong>: When testing on datasets, why are the authors using such a low label rate?</p>
  <p><strong>Context</strong>: In Table 1 where the authors list the statistics for the datasets they used to benchmark the architecture, the label rates are quite small percentages (5%, 3%, &lt;1%). I don't quite understand why this is the case...</p>
  <p>I know this is 'semi-supervised' classification such that we have access to ALL the node features, regardless of whether they are training or testing. However, I don't know why we are using 5% label rate as opposed to, for example, 30% label rate.</p>
  <p>Any insight would be appreciated.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-02-01T18:05:28.353Z
Comments.Text: a main reason is that it keeps the computational cost down. I’m not aware of any performance degradations by using more labels, the only thing I can think is that if you achieve similar test performance whilst training on fewer labels, this would be better as you’re less likely to be overfitting
Comments.UserDisplayName: David
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-02-02T10:01:50.880Z
Comments.Text: @DavidIreland - many thanks for the reply! That makes sense.
Comments.UserDisplayName: Rocky the Owl
Tags: <deep-learning><graph-neural-networks>
Title: Graph Neural Networks: Why do papers use very low label rates?
UserDisplayName: Rocky the Owl
ThreadText: # Graph Neural Networks: Why do papers use very low label rates?
  
  (Asked by: username_0 on 2022-02-01)
  
  I was recently reading the following paper: "Semi-supervised classification with Graph Convolutional Networks" by Kipf and Welling ([here](https://arxiv.org/pdf/1609.02907.pdf)).
  
  **Question**: When testing on datasets, why are the authors using such a low label rate?
  
  **Context**: In Table 1 where the authors list the statistics for the datasets they used to benchmark the architecture, the label rates are quite small percentages (5%, 3%, <1%). I don't quite understand why this is the case...
  
  I know this is 'semi-supervised' classification such that we have access to ALL the node features, regardless of whether they are training or testing. However, I don't know why we are using 5% label rate as opposed to, for example, 30% label rate.
  
  Any insight would be appreciated.
  
  **Tags:** deep-learning, graph-neural-networks
  > **Comments:**
  > **username_1**: a main reason is that it keeps the computational cost down. I’m not aware of any performance degradations by using more labels, the only thing I can think is that if you achieve similar test performance whilst training on fewer labels, this would be better as you’re less likely to be overfitting  
  > **username_0**: @DavidIreland - many thanks for the reply! That makes sense.  
  
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