Signed graphs serve as fundamental data structures for representing positive and negative relationships in social networks, with signed graph neural networks (SGNNs) emerging as the primary tool for their analysis. Our investigation reveals that balance theory, while essential for modeling signed relationships in SGNNs, inadvertently introduces exploitable vulnerabilities to black-box attacks. To demonstrate this vulnerability, we propose balance-attack, a novel adversarial strategy specifically designed to compromise graph balance degree, and develop an efficient heuristic algorithm to solve the associated NP-hard optimization problem. While existing approaches attempt to restore attacked graphs through balance learning techniques, they face a critical challenge we term "Irreversibility of Balance-related Information," where restored edges fail to align with original attack targets. To address this limitation, we introduce Balance Augmented-Signed Graph Contrastive Learning (BA-SGCL), an innovative framework that combines contrastive learning with balance augmentation techniques to achieve robust graph representations. By maintaining high balance degree in the latent space, BA-SGCL effectively circumvents the irreversibility challenge and enhances model resilience. Extensive experiments across multiple SGNN architectures and real-world datasets demonstrate both the effectiveness of our proposed balance-attack and the superior robustness of BA-SGCL, advancing the security and reliability of signed graph analysis in social networks. Datasets and codes of the proposed framework are at the github repository https://anonymous.4open.science/r/BA-SGCL-submit-DF41/.

翻译：有符号图作为表示社交网络中正负关系的基础数据结构，其分析主要依赖于有符号图神经网络（SGNNs）。我们的研究发现，平衡理论虽然在SGNNs中建模符号关系至关重要，却无意中引入了可利用的黑盒攻击漏洞。为证明此漏洞，我们提出平衡攻击——一种专门设计用于破坏图平衡度的新型对抗策略，并开发了高效的启发式算法以解决相关的NP难优化问题。现有方法试图通过平衡学习技术恢复受攻击图，却面临我们称为"平衡相关信息的不可逆性"的关键挑战，即恢复的边无法与原始攻击目标对齐。为解决此局限，我们提出了平衡增强-有符号图对比学习（BA-SGCL），这是一个将对比学习与平衡增强技术相结合的创新框架，旨在获得鲁棒的图表示。通过在潜在空间中保持高平衡度，BA-SGCL有效规避了不可逆性挑战并增强了模型韧性。在多种SGNN架构和真实数据集上的大量实验表明，我们提出的平衡攻击具有显著效果，且BA-SGCL展现出卓越的鲁棒性，从而推动了社交网络中有符号图分析的安全性与可靠性发展。所提框架的数据集与代码存放于github仓库 https://anonymous.4open.science/r/BA-SGCL-submit-DF41/。