<p>Semi-supervised learning means that you get both labelled and unlabelled data as your input. The graph part means that there is also a relation defined for your datapoints. What exactly it is that you learn and how you use the graph structure depends on the problem. Here is a thorough exploration of the subject: <a href="http://www.cs.cmu.edu/~zhuxj/pub/thesis.pdf">http://www.cs.cmu.edu/~zhuxj/pub/thesis.pdf</a></p>

CC BY-SA 3.0
2012-01-05T05:50:21.330Z
2012-01-05T05:50:21.330Z
Don Reba
<p>What is semi-supervised learning on graphs? We have been told that if we just have a function which has an input graph, or a given graph with labeled nodes, we should be able to predict labels on other nodes of the graph. This is not clear.</p>

CC BY-SA 3.0
2012-01-06T06:22:45.117Z
Why the down votes? The question may not be formulated properly, but there's substance in it?
Kris
CC BY-SA 3.0
2014-03-27T15:01:46.287Z
yes it was not clear. were you told in a class by an instructor? it seems to be a general case of semi supervised learning, applied to labels on graphs.
vzn
<graph-theory><machine-learning>
Semi-supervised learning on graphs
ashim
# Semi-supervised learning on graphs

(Asked by: username_0 on 2012-01-05)

What is semi-supervised learning on graphs? We have been told that if we just have a function which has an input graph, or a given graph with labeled nodes, we should be able to predict labels on other nodes of the graph. This is not clear.

**Tags:** graph-theory, machine-learning
> **Comments:**
> **username_1**: Why the down votes? The question may not be formulated properly, but there's substance in it?  
> **username_2**: yes it was not clear. were you told in a class by an instructor? it seems to be a general case of semi supervised learning, applied to labels on graphs.  

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## 1 answers

### Answer by username_3 on 2012-01-05. Score: 6 | Accepted answer

Semi-supervised learning means that you get both labelled and unlabelled data as your input. The graph part means that there is also a relation defined for your datapoints. What exactly it is that you learn and how you use the graph structure depends on the problem. Here is a thorough exploration of the subject: <http://www.cs.cmu.edu/~zhuxj/pub/thesis.pdf>


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