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Table 5 Chosen hyperparameters for all GNN models except GAT

From: The interplay between communities and homophily in semi-supervised classification using graph neural networks

  Dataset Dropout Learning rate Regularization weight
GCN CORA-ML 0.8 0.010 0.0100
Citeseer 0.8 0.010 0.0100
Pubmed 0.8 0.010 0.0100
CORA-Full 0.2 0.005 0.0010
Actor 0.8 0.010 0.0100
Squirrel 0.4 0.010 0.0100
Texas 0.8 0.010 0.0100
Wisconsin 0.8 0.010 0.0100
SAGE CORA-ML 0.8 0.010 0.0100
Citeseer 0.8 0.010 0.0100
Pubmed 0.8 0.010 0.0100
CORA-Full 0.8 0.005 0.0010
Actor 0.8 0.010 0.0100
Squirrel 0.4 0.005 0.1000
Texas 0.8 0.010 0.0100
Wisconsin 0.8 0.010 0.0100
APPNP CORA-ML 0.8 0.010 0.0100
Citeseer 0.8 0.010 0.0100
Pubmed 0.8 0.010 0.0100
CORA-Full 0.2 0.005 0.0001
Actor 0.8 0.010 0.0100
Squirrel 0.2 0.010 0.0010
Texas 0.8 0.010 0.0100
Wisconsin 0.8 0.010 0.0100
SGC CORA-ML 0.8 0.010 0.0100
Citeseer 0.8 0.010 0.0100
Pubmed 0.8 0.010 0.0100
CORA-Full 0.2 0.005 0.0010
Actor 0.8 0.010 0.0100
Squirrel 0.4 0.010 0.0100
Texas 0.8 0.010 0.0100
Wisconsin 0.8 0.010 0.0100
CGCN CORA-ML 0.8 0.001 0.1000
Citeseer 0.8 0.001 0.1000
Pubmed 0.6 0.010 0.0001
CORA-Full 0.8 0.001 0.1000
Actor 0.6 0.010 0.1000
Squirrel 0.6 0.005 0.1000
Texas 0.8 0.010 0.1000
Wisconsin 0.6 0.005 0.1000