DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks

Signed graphs can model friendly or antagonistic relations where edges are annotated with a positive or negative sign. The main downstream task in signed graph analysis is \textit{link sign prediction} . Signed Graph Neural Networks (SGNNs) have been widely used for signed graph representation learning. While significant progress has been made in SGNNs research, two issues (i.e., graph sparsity and unbalanced triangles) persist in the current SGNN models. We aim to alleviate these issues through data augmentation ( \textit{DA}) techniques which have demonstrated effectiveness in improving the performance of graph neural networks.