DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

Understand

\emph{Over-fitting} and \emph{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.
• This paper proposes DropEdge, a novel and flexible technique to alleviate both issues.
• At its core, DropEdge randomly removes a certain number of edges from the input graph at each training epoch, acting like a data augmenter and also a message passing reducer.