Hi,
I want to split my graph into connected components because I don’t want to break my network when training my model. There is a notion of time in those graphs. I want to point out that I want to do a node classification and not a graph classification.
Moreover, I would like that my training and testing set have approximately the same proportion of nodes with label -1, 0 and 1.
Is there any function in dgl which allows to do this or not ?
Thank you !
NetworkX to achieve the construction of connected components, and then utilize DGL’s interfaces to convert the graph structure.g.ndata to store; it doesn’t seem difficult.Hi @BearBiscuit05 and thanks for your answer !
I had the same intuition for the first remark, i have already done it.
The second part is harder as you have to use a stratify parameter to get globally two subgraphs with the same proportions of labels.
If any of you have an idea on how to implement something like this, I will be happy to hear from you.
It is not that easy to implement in the end.
Thanks !
Actually I don’t quite understand your problem. Do you want to split a graph into three disconnected subgraphs and discard the edges across the subgraphs?
Not at all.
I have a complete graph which has multiple connected components. I want those connected components to go in a training set and a testing set. I also want them to have a similar percentage of nodes with label 0 and 1.
Have you any idea on how I could code this ?
I see. It is look like a Knapsack problem if you regard each connected graph as an object with some number of label 0 or 1. You want to divide the graphs into two sets and each set has the similar numbers of 0 and 1. DGL don’t provide this functionality. So you may need to find out a solution for this Knapsack problem.
Thanks a lot for your response !
I’ll definitely have a look into this knspsack problem.
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