Signed graphs are well-suited for modeling social networks as they capture both positive and negative relationships. Signed graph neural networks (SGNNs) are commonly employed to predict link signs (i.e., positive and negative) in such graphs due to their ability to handle the unique structure of signed graphs. However, real-world signed graphs are vulnerable to malicious attacks by manipulating edge relationships, and existing adversarial graph attack methods do not consider the specific structure of signed graphs. SGNNs often incorporate balance theory to effectively model the positive and negative links. Surprisingly, we find that the balance theory that they rely on can ironically be exploited as a black-box attack. In this paper, we propose a novel black-box attack called balance-attack that aims to decrease the balance degree of the signed graphs. We present an efficient heuristic algorithm to solve this NP-hard optimization problem. We conduct extensive experiments on five popular SGNN models and four real-world datasets to demonstrate the effectiveness and wide applicability of our proposed attack method. By addressing these challenges, our research contributes to a better understanding of the limitations and resilience of robust models when facing attacks on SGNNs. This work contributes to enhancing the security and reliability of signed graph analysis in social network modeling. Our PyTorch implementation of the attack is publicly available on GitHub: https://github.com/JialongZhou666/Balance-Attack.git.

翻译：有符号图因其同时捕获正向和负向关系，非常适合用于社交网络建模。有符号图神经网络（SGNN）常被用于预测此类图中边的符号（即正向和负向），因为它们能够处理有符号图的独特结构。然而，现实世界中的有符号图容易通过操纵边关系遭受恶意攻击，而现有的对抗性图攻击方法并未考虑有符号图的特定结构。SGNN通常结合平衡理论来有效建模正向和负向边。令人惊讶的是，我们发现它们所依赖的平衡理论竟可能被反过来利用为一种黑盒攻击。在本文中，我们提出一种名为balance-attack的新型黑盒攻击方法，其旨在降低有符号图的平衡度。我们提出一种高效的启发式算法来解决这个NP难优化问题。我们在五种流行的SGNN模型和四个真实世界数据集上进行了大量实验，以证明我们提出的攻击方法的有效性和广泛适用性。通过解决这些挑战，我们的研究有助于更好地理解鲁棒模型在面对SGNN攻击时的局限性和韧性。这项工作有助于增强社交网络建模中有符号图分析的安全性和可靠性。我们的PyTorch攻击实现已在GitHub上公开：https://github.com/JialongZhou666/Balance-Attack.git。