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TUs

Details:

  • Split total number of graphs into 3 (train, val and test) in 80:10:10
  • Stratified split proportionate to original distribution of data with respect to classes
  • Using sklearn to perform the split and then save the indexes
  • Preparing 10 such combinations of indexes split to be used in Graph NNs
  • As with KFold, each of the 10 fold have unique test set.