We propose GNNInfer, the first automatic property inference technique for GNNs. To tackle the challenge of varying input structures in GNNs, GNNInfer first identifies a set of representative influential structures that contribute significantly towards the prediction of a GNN. Using these structures, GNNInfer converts each pair of an influential structure and the GNN to their equivalent FNN and then leverages existing property inference techniques to effectively capture properties of the GNN that are specific to the influential structures. GNNINfer then generalizes the captured properties to any input graphs that contain the influential structures. Finally, GNNInfer improves the correctness of the inferred properties by building a model (either a decision tree or linear regression) that estimates the deviation of GNN output from the inferred properties given full input graphs. The learned model helps GNNInfer extend the inferred properties with constraints to the input and output of the GNN, obtaining stronger properties that hold on full input graphs. Our experiments show that GNNInfer is effective in inferring likely properties of popular real-world GNNs, and more importantly, these inferred properties help effectively defend against GNNs' backdoor attacks. In particular, out of the 13 ground truth properties, GNNInfer re-discovered 8 correct properties and discovered likely correct properties that approximate the remaining 5 ground truth properties. Using properties inferred by GNNInfer to defend against the state-of-the-art backdoor attack technique on GNNs, namely UGBA, experiments show that GNNInfer's defense success rate is up to 30 times better than existing baselines.

翻译：我们提出GNNInfer，这是首个针对GNN的自动属性推断技术。为应对GNN中输入结构多变这一挑战，GNNInfer首先识别出一组对GNN预测结果具有显著影响的代表性结构。利用这些结构，GNNInfer将每个影响结构与GNN的配对转化为等效的前馈神经网络（FNN），进而利用现有属性推断技术有效捕获该GNN在特定影响结构下的属性。随后，GNNInfer将所捕获的属性泛化到包含这些影响结构的任意输入图中。最后，GNNInfer通过构建一个模型（决策树或线性回归）来估计基于完整输入图时GNN输出与推断属性之间的偏差，从而提升推断属性的正确性。该学习模型帮助GNNInfer为推断属性添加输入与输出约束，获得适用于完整输入图的更强属性。实验表明，GNNInfer能有效推断真实世界流行GNN的近似属性，且这些推断属性有助于高效防御针对GNN的后门攻击。具体而言，在13个真实属性中，GNNInfer重新发现了8个正确属性，并为其余5个真实属性发现了近似正确的属性。利用GNNInfer推断的属性防御当前最先进的GNN后门攻击技术UGBA时，实验显示GNNInfer的防御成功率较现有基线方法最高提升30倍。