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

• URL: http://arxiv.org/abs/2607.16245v1
• Date: Fri, 26 Jun 2026 12:30:31 GMT
• Title: Learning Structural Manipulability in Gate-Level Netlists Using Graph Neural Networks
• Abstract summary: Gate-level netlists exhibit intrinsic structural properties that influence signal propagation independently of functional simulation.<n>We define a topology-driven structural manipulability score that characterizes node-level structural flexibility using path participation, k-core embedding, symmetry, and centrality.
• Score: 5.487972363969534
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Structure-Feature Aligned Graph Learning via Alternating Constrained Optimization [7.881713230406407]
  We introduce a constrained two-view framework for node prediction that aligns structure-conditioned GNN embeddings with a structure-free feature prior learned by an anchor model.<n>We also propose a Channel-Split Adaptive Gated GNN (CSAG-GNN) that dynamically routes representations between global spectral smoothing and local spatial discrimination through a node-wise gating mechanism.
  arXiv  Detail & Related papers  (2026-07-13T13:58:52Z)
• On the Impact of Graph Neural Networks in Recommender Systems: A Topological Perspective [49.391877616394765]
  In recommender systems, user-item interactions can be modeled as a bipartite graph, where user and item nodes are connected by undirected edges.<n>This graph-based view has motivated the rapid adoption of graph neural networks (GNNs)<n>Despite their empirical success, the reasons why GNNs offer systematic advantages over other approaches remain only partially understood.
  arXiv  Detail & Related papers  (2025-12-08T10:19:43Z)
• Parameter-Free Structural-Diversity Message Passing for Graph Neural Networks [8.462209415744098]
  Graph Neural Networks (GNNs) have shown remarkable performance in structured data modeling tasks such as node classification.<n>This paper proposes a parameter-free graph neural network framework based on structural diversity.<n>The framework is inspired by structural diversity theory and designs a unified structural-diversity message passing mechanism.
  arXiv  Detail & Related papers  (2025-08-27T13:42:45Z)
• Graph Self-Supervised Learning with Learnable Structural and Positional Encodings [39.20899720477907]
  We introduce emphGenHopNet, a GNN framework that integrates a $k$-hop message-passing scheme.<n>We also propose a structural- and positional-aware GSSL framework that incorporates topological information throughout the learning process.<n>Our work significantly advances GSSL's capability in distinguishing graphs with similar local structures but different global topologies.
  arXiv  Detail & Related papers  (2025-02-22T14:10:06Z)
• Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning [56.22841701016295]
  Supervised Causal Learning (SCL) is an emerging paradigm in this field.<n>Existing Deep Neural Network (DNN)-based methods commonly adopt the "Node-Edge approach"
  arXiv  Detail & Related papers  (2025-02-15T19:10:35Z)
• Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting [50.181824673039436]
  We propose a Graph Structure Self-Contrasting (GSSC) framework that learns graph structural information without message passing. The proposed framework is based purely on Multi-Layer Perceptrons (MLPs), where the structural information is only implicitly incorporated as prior knowledge. It first applies structural sparsification to remove potentially uninformative or noisy edges in the neighborhood, and then performs structural self-contrasting in the sparsified neighborhood to learn robust node representations.
  arXiv  Detail & Related papers  (2024-09-09T12:56:02Z)
• Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN Performance [0.5416466085090772]
  We introduce a comprehensive benchmarking framework for graph machine learning. We generate synthetic networks with realistic topological properties and node feature vectors. Results highlight the dependency of model performance on the interplay between network structure and node features.
  arXiv  Detail & Related papers  (2024-06-04T20:40:06Z)
• Learning Invariant Representations of Graph Neural Networks via Cluster Generalization [58.68231635082891]
  Graph neural networks (GNNs) have become increasingly popular in modeling graph-structured data. In this paper, we experimentally find that the performance of GNNs drops significantly when the structure shift happens. We propose the Cluster Information Transfer (CIT) mechanism, which can learn invariant representations for GNNs.
  arXiv  Detail & Related papers  (2024-03-06T10:36:56Z)
• Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples [1.0507062889290775]
  We propose a signal-driven self-supervised method for graph representation learning. It uses a topological information-guided structural equivalence sampling strategy. The results show that the model performance can be effectively improved.
  arXiv  Detail & Related papers  (2022-12-08T16:04:06Z)
• Hierarchical Graph Capsule Network [78.4325268572233]
  We propose hierarchical graph capsule network (HGCN) that can jointly learn node embeddings and extract graph hierarchies. To learn the hierarchical representation, HGCN characterizes the part-whole relationship between lower-level capsules (part) and higher-level capsules (whole)
  arXiv  Detail & Related papers  (2020-12-16T04:13:26Z)
• Progressive Graph Convolutional Networks for Semi-Supervised Node Classification [97.14064057840089]
  Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification. We propose a method to automatically build compact and task-specific graph convolutional networks.
  arXiv  Detail & Related papers  (2020-03-27T08:32:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.