# Learning Structural Manipulability in Gate-Level Netlists Using Graph Neural Networks

arXiv: https://arxiv.org/abs/2607.16245 · HTML: https://mlsanity.com/paper/2607.16245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 26 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Wasserstein Weisfeiler-Lehman Graph Kernels](https://mlsanity.com/paper/1906.01277/) (2019)
- [Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction](https://mlsanity.com/paper/2206.04216/) (2022)
- [CktGNN: Circuit Graph Neural Network for Electronic Design Automation](https://mlsanity.com/paper/2308.16406/) (2023)
- [Hardware Trojan Detection using Graph Neural Networks](https://mlsanity.com/paper/2204.11431/) (2022)
- [Embracing Graph Neural Networks for Hardware Security (Invited Paper)](https://mlsanity.com/paper/2208.08554/) (2022)
- [HybridNet: Dual-Branch Fusion of Geometrical and Topological Views for VLSI Congestion Prediction](https://mlsanity.com/paper/2305.05374/) (2023)

## Abstract (arXiv)

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.