<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>

CC BY-SA 4.0
2022-02-01T18:05:28.353Z
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
David
CC BY-SA 4.0
2022-02-02T10:01:50.880Z
@DavidIreland - many thanks for the reply! That makes sense.
Rocky the Owl
<deep-learning><graph-neural-networks>
Graph Neural Networks: Why do papers use very low label rates?
Rocky the Owl
# 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**: @username_1Ireland - many thanks for the reply! That makes sense.  

---

## 0 answers

