# Graph Neural Networks Based Analog Circuit Link Prediction

arXiv: https://arxiv.org/abs/2504.10240 · HTML: https://mlsanity.com/paper/2504.10240/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 61 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 27 of 27)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Large Language Models for Mathematical Reasoning: Progresses and Challenges](https://mlsanity.com/paper/2402.00157/) (2024)
- [GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models](https://mlsanity.com/paper/2309.10730/) (2023)
- [Classification using Hyperdimensional Computing: A Review](https://mlsanity.com/paper/2004.11204/) (2020)
- [AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs](https://mlsanity.com/paper/2001.01808/) (2020)
- [Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/2103.03642/) (2021)
- [Puzzle Solving using Reasoning of Large Language Models: A Survey](https://mlsanity.com/paper/2402.11291/) (2024)
- [ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models](https://mlsanity.com/paper/2406.18770/) (2024)
- [Co-embedding of Nodes and Edges with Graph Neural Networks](https://mlsanity.com/paper/2010.13242/) (2020)
- [Learning Latent Block Structure in Weighted Networks](https://mlsanity.com/paper/1404.0431/) (2014)
- [LLMs for Relational Reasoning: How Far are We?](https://mlsanity.com/paper/2401.09042/) (2024)
- [VerilogReader: LLM-Aided Hardware Test Generation](https://mlsanity.com/paper/2406.04373/) (2024)
- [Equivariant Hypergraph Neural Networks](https://mlsanity.com/paper/2208.10428/) (2022)
- [Classification of Edge-dependent Labels of Nodes in Hypergraphs](https://mlsanity.com/paper/2306.03032/) (2023)
- [Sampling Enclosing Subgraphs for Link Prediction](https://mlsanity.com/paper/2206.12004/) (2022)
- [Learning to Design Analog Circuits to Meet Threshold Specifications](https://mlsanity.com/paper/2307.13861/) (2023)
- [SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning](https://mlsanity.com/paper/2303.03379/) (2023)
- [DE-HNN: An effective neural model for Circuit Netlist representation](https://mlsanity.com/paper/2404.00477/) (2024)
- [Learning the effective order of a hypergraph dynamical system](https://mlsanity.com/paper/2306.01813/) (2023)
- [Graph Attention-Based Symmetry Constraint Extraction for Analog Circuits](https://mlsanity.com/paper/2312.14405/) (2023)
- [Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods](https://mlsanity.com/paper/2411.14711/) (2024)
- [Message-Passing on Hypergraphs: Detectability, Phase Transitions and Higher-Order Information](https://mlsanity.com/paper/2312.00708/) (2023)
- [Finding irrelevant vertices in linear time on bounded-genus graphs](https://mlsanity.com/paper/1907.05940/) (2019)
- [Inherent Limits on Topology-Based Link Prediction](https://mlsanity.com/paper/2301.08792/) (2023)
- [Python Framework for Modular and Parametric SPICE Netlists Generation](https://mlsanity.com/paper/2306.12224/) (2023)

## Abstract (arXiv)

Circuit link prediction, which identifies missing component connections from incomplete netlists, is crucial in analog circuit design automation. However, existing methods face three main challenges: 1) Insufficient use of topological patterns in circuit graphs reduces prediction accuracy; 2) Data scarcity due to the complexity of annotations hinders model generalization; 3) Limited adaptability to various netlist formats restricts model flexibility. We propose Graph Neural Networks Based Analog Circuit Link Prediction (GNN-ACLP), a graph neural networks (GNNs) based method featuring three innovations to tackle these challenges. First, we introduce the SEAL (learning from Subgraphs, Embeddings, and Attributes for Link prediction) framework and achieve port-level accuracy in circuit link prediction. Second, we propose Netlist Babel Fish, a netlist format conversion tool that leverages retrieval-augmented generation (RAG) with a large language model (LLM) to enhance the compatibility of netlist formats. Finally, we build a comprehensive dataset, SpiceNetlist, comprising 775 annotated circuits of 7 different types across 10 component classes. Experiments demonstrate accuracy improvements of 16.08% on SpiceNetlist, 11.38% on Image2Net, and 16.01% on Masala-CHAI compared to the baseline in intra-dataset evaluation, while maintaining accuracy from 92.05% to 99.07% in cross-dataset evaluation, demonstrating robust feature transfer capabilities. However, its linear computational complexity makes processing large-scale netlists challenging and requires future addressing.