# BoolSkeleton: Boolean Network Skeletonization via Homogeneous Pattern Reduction

arXiv: https://arxiv.org/abs/2511.02196 · HTML: https://mlsanity.com/paper/2511.02196/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 45 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Builds on (most cited first, 21 of 21)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation](https://mlsanity.com/paper/2402.03358/) (2024)
- [Spectrally approximating large graphs with smaller graphs](https://mlsanity.com/paper/1802.07510/) (2018)
- [Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2204.07321/) (2022)
- [DeepGate: Learning Neural Representations of Logic Gates](https://mlsanity.com/paper/2111.14616/) (2021)
- [DeepGate2: Functionality-Aware Circuit Representation Learning](https://mlsanity.com/paper/2305.16373/) (2023)
- [OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis](https://mlsanity.com/paper/2110.11292/) (2021)
- [DeepGate3: Towards Scalable Circuit Representation Learning](https://mlsanity.com/paper/2407.11095/) (2024)
- [MasterRTL: A Pre-Synthesis PPA Estimation Framework for Any RTL Design](https://mlsanity.com/paper/2311.08441/) (2023)
- [LOSTIN: Logic Optimization via Spatio-Temporal Information with Hybrid Graph Models](https://mlsanity.com/paper/2201.08455/) (2022)
- [iEDA: An Open-Source Intelligent Physical Implementation Toolkit and Library](https://mlsanity.com/paper/2308.01857/) (2023)
- [Multimodal Fake News Detection: MFND Dataset and Shallow-Deep Multitask Learning](https://mlsanity.com/paper/2505.06796/) (2025)
- [Graph Similarity Description: How Are These Graphs Similar?](https://mlsanity.com/paper/2105.14364/) (2021)
- [OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis](https://mlsanity.com/paper/2411.09422/) (2024)
- [Enhanced Fast Boolean Matching based on Sensitivity Signatures Pruning](https://mlsanity.com/paper/2111.06213/) (2021)
- [BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation](https://mlsanity.com/paper/2401.10753/) (2024)
- [MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization](https://mlsanity.com/paper/2409.06077/) (2024)
- [Boolean-aware Boolean Circuit Classification: A Comprehensive Study on Graph Neural Network](https://mlsanity.com/paper/2411.10481/) (2024)

## Abstract (arXiv)

Boolean equivalence allows Boolean networks with identical functionality to exhibit diverse graph structures. This gives more room for exploration in logic optimization, while also posing a challenge for tasks involving consistency between Boolean networks. To tackle this challenge, we introduce BoolSkeleton, a novel Boolean network skeletonization method that improves the consistency and reliability of design-specific evaluations. BoolSkeleton comprises two key steps: preprocessing and reduction. In preprocessing, the Boolean network is transformed into a defined Boolean dependency graph, where nodes are assigned the functionality-related status. Next, the homogeneous and heterogeneous patterns are defined for the node-level pattern reduction step. Heterogeneous patterns are preserved to maintain critical functionality-related dependencies, while homogeneous patterns can be reduced. Parameter K of the pattern further constrains the fanin size of these patterns, enabling fine-tuned control over the granularity of graph reduction. To validate BoolSkeleton's effectiveness, we conducted four analysis/downstream tasks around the Boolean network: compression analysis, classification, critical path analysis, and timing prediction, demonstrating its robustness across diverse scenarios. Furthermore, it improves above 55% in the average accuracy compared to the original Boolean network for the timing prediction task. These experiments underscore the potential of BoolSkeleton to enhance design consistency in logic synthesis.