  Signed graphs consist of edges and signs, which can be separated into
structural information and balance-related information, respectively. Existing
signed graph neural networks (SGNNs) typically rely on balance-related
information to generate embeddings. Nevertheless, the emergence of recent
adversarial attacks has had a detrimental impact on the balance-related
information. Similar to how structure learning can restore unsigned graphs,
balance learning can be applied to signed graphs by improving the balance
degree of the poisoned graph. However, this approach encounters the challenge
"Irreversibility of Balance-related Information" - while the balance degree
improves, the restored edges may not be the ones originally affected by
attacks, resulting in poor defense effectiveness. To address this challenge, we
propose a robust SGNN framework called Balance Augmented-Signed Graph
Contrastive Learning (BA-SGCL), which combines Graph Contrastive Learning
principles with balance augmentation techniques. Experimental results
demonstrate that BA-SGCL not only enhances robustness against existing
adversarial attacks but also achieves superior performance on link sign
prediction task across various datasets.
