SGA: A Graph Augmentation Method for Signed Graph Neural Networks
Zeyu Zhang Shuyan Wan Sijie Wang Xianda Zheng Xinrui Zhang Kaiqi Zhao Jiamou Liu Dong Hao
Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns
in real-world signed graphs containing positive and negative links. However,
three key challenges hinder current SGNN-based signed graph representation
learning: sparsity in signed graphs leaves latent structures undiscovered,
unbalanced triangles pose representation difficulties for SGNN models, and
real-world signed graph datasets often lack supplementary information like node
labels and features. These constraints limit the potential of SGNN-based
representation learning. We address these issues with data augmentation
techniques. Despite many graph data augmentation methods existing for unsigned
graphs, none are tailored for signed graphs. Our paper introduces the novel
Signed Graph Augmentation framework (SGA), comprising three main components.
First, we employ the SGNN model to encode the signed graph, extracting latent
structural information for candidate augmentation structures. Second, we
evaluate these candidate samples (edges) and select the most beneficial ones
for modifying the original training set. Third, we propose a novel augmentation
perspective that assigns varying training difficulty to training samples,
enabling the design of a new training strategy. Extensive experiments on six
real-world datasets (Bitcoin-alpha, Bitcoin-otc, Epinions, Slashdot, Wiki-elec,
and Wiki-RfA) demonstrate that SGA significantly improves performance across
multiple benchmarks. Our method outperforms baselines by up to 22.2% in AUC for
SGCN on Wiki-RfA, 33.3% in F1-binary, 48.8% in F1-micro, and 36.3% in F1-macro
for GAT on Bitcoin-alpha in link sign prediction.