DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

ICLR 2020 Anonymous

Over-fitting and over-smoothing are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification. In particular, over-fitting weakens the generalization ability on small dataset, while over-smoothing impedes model training by isolating output representations from the input features with the increase in network depth... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Node Classification Citeseer Full-supervised IncepGCN+DropEdge Accuracy 80.50% # 1
Node Classification CiteSeer with Public Split: fixed 20 nodes per class IncepGCN+DropEdge Accuracy 72.70% # 9
Node Classification Cora Full-supervised IncepGCN+DropEdge Accuracy 88.2% # 1
Node Classification Cora with Public Split: fixed 20 nodes per class IncepGCN+DropEdge Accuracy 83.50% # 7
Node Classification Pubmed Full-supervised GraphSAGE+DropEdge Accuracy 91.70% # 1
Node Classification PubMed with Public Split: fixed 20 nodes per class GCN+DropEdge Accuracy 79.60% # 7
Node Classification Reddit JKNet+DropEdge Accuracy 97.02% # 1