Graph neural networks (GNNs) are vulnerable to adversarial attacks, especially for topology perturbations, and many methods that improve the robustness of GNNs have received considerable attention. Recently, we have witnessed the significant success of large language models (LLMs), leading many to explore the great potential of LLMs on GNNs. However, they mainly focus on improving the performance of GNNs by utilizing LLMs to enhance the node features. Therefore, we ask: Will the robustness of GNNs also be enhanced with the powerful understanding and inference capabilities of LLMs? By presenting the empirical results, we find that despite that LLMs can improve the robustness of GNNs, there is still an average decrease of 23.1% in accuracy, implying that the GNNs remain extremely vulnerable against topology attacks. Therefore, another question is how to extend the capabilities of LLMs on graph adversarial robustness. In this paper, we propose an LLM-based robust graph structure inference framework, LLM4RGNN, which distills the inference capabilities of GPT-4 into a local LLM for identifying malicious edges and an LM-based edge predictor for finding missing important edges, so as to recover a robust graph structure. Extensive experiments demonstrate that LLM4RGNN consistently improves the robustness across various GNNs. Even in some cases where the perturbation ratio increases to 40%, the accuracy of GNNs is still better than that on the clean graph. The source code can be found in https://github.com/zhongjian-zhang/LLM4RGNN.

翻译：图神经网络（GNNs）易受对抗攻击的影响，尤其是针对拓扑结构的扰动，因此许多提升GNN鲁棒性的方法受到了广泛关注。近年来，大型语言模型（LLMs）取得了显著成功，促使众多研究者探索LLMs在图神经网络上的巨大潜力。然而，现有工作主要集中于利用LLMs增强节点特征以提升GNN的性能。因此，我们提出疑问：借助LLMs强大的理解与推理能力，GNN的鲁棒性是否也能得到增强？通过实证结果我们发现，尽管LLMs能够在一定程度上提升GNN的鲁棒性，但其准确率平均仍下降23.1%，这表明GNN在面对拓扑攻击时依然极为脆弱。因此，另一个问题随之而来：如何扩展LLMs在图对抗鲁棒性方面的能力？本文提出一种基于LLM的鲁棒图结构推断框架LLM4RGNN，该框架将GPT-4的推理能力蒸馏至一个本地LLM用于识别恶意边，并利用一个基于语言模型的边预测器来发现缺失的重要边，从而恢复出鲁棒的图结构。大量实验表明，LLM4RGNN能够持续提升多种GNN的鲁棒性。即使在某些扰动比例增至40%的情况下，GNN的准确率仍优于其在干净图上的表现。源代码可在 https://github.com/zhongjian-zhang/LLM4RGNN 获取。