AI 中文总结
研究门级网表中结构可操纵性，定义拓扑驱动分数，用图神经网络制定节点级回归学习该分数，实验评估不同GNN架构效果，组件级和消融分析因素贡献，案例研究揭示注入木马电路结构模式。
AI 中文摘要
门级网表展现出独立于功能模拟影响信号传播的内在结构属性。我们定义了一个拓扑驱动的结构可操纵性分数，它使用路径参与、k核嵌入、对称性和中心性来表征节点级结构灵活性。将网表建模为有向图，我们制定节点级回归以使用图神经网络（GNN）学习此拓扑衍生分数。在ISCAS85和EPFL基准上的实验评估了不同GNN架构在保留电路上近似此度量的有效性，层次模型产生最一致的排名。组件级和消融分析检查了各个因素的贡献。作为一个说明性案例研究，使用TrustHub模板对注入木马的电路进行分析揭示了统计学上可区分的结构模式，表明基于拓扑的评分提供了互补的结构见解。
英文摘要
Gate-level netlists exhibit intrinsic structural properties that influence signal propagation independently of functional simulation. We define a topology-driven structural manipulability score that characterizes node-level structural flexibility using path participation, k-core embedding, symmetry, and centrality. Modeling netlists as directed graphs, we formulate node-level regression to learn this topology-derived score using graph neural networks (GNNs). Experiments on ISCAS85 and EPFL benchmarks evaluate how effectively different GNN architectures approximate this metric across held-out circuits, with hierarchical models yielding the most consistent rankings. Component-level and ablation analyses examine the contribution of individual factors. As an illustrative case study, analysis of Trojan-injected circuits using TrustHub templates reveals statistically distinguishable structural patterns, indicating that topology-based scoring provides complementary structural insight.
Comments11 pages
