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

TL;DR

This paper introduces the Signed Graph Augmentation (SGA) framework to improve signed graph neural networks by addressing data sparsity and imbalance issues, demonstrating significant performance gains over existing methods.

Contribution

The paper proposes a novel SGA framework with structure augmentation and candidate selection strategies, specifically designed for signed graphs, enhancing SGNN training effectiveness.

Findings

SGA improves SGNN performance significantly.

32.3% F1-micro improvement on Slashdot dataset.

Random DropEdge does not enhance signed graph tasks.

Abstract

The paper discusses signed graphs, which model friendly or antagonistic relationships using edges marked with positive or negative signs, focusing on the task of link sign prediction. While Signed Graph Neural Networks (SGNNs) have advanced, they face challenges like graph sparsity and unbalanced triangles. The authors propose using data augmentation (DA) techniques to address these issues, although many existing methods are not suitable for signed graphs due to a lack of side information. They highlight that the random DropEdge method, a rare DA approach applicable to signed graphs, does not enhance link sign prediction performance. In response, they introduce the Signed Graph Augmentation (SGA) framework, which includes a structure augmentation module to identify candidate edges and a strategy for selecting beneficial candidates, ultimately improving SGNN training. Experimental results show that SGA significantly boosts the performance of SGNN models, with a notable 32.3% improvement in F1-micro for SGCN on the Slashdot dataset.