GNN-ACLP is a novel framework that leverages graph neural networks to predict missing component connections in analog circuit designs, addressing key challenges in circuit automation.

• Utilizes topological patterns in circuit graphs to improve prediction accuracy
• Overcomes data scarcity challenges through advanced modeling techniques
• Provides adaptability across various netlist formats
• Streamlines the circuit design process through automation

This research significantly advances engineering capabilities by reducing manual effort in circuit design, potentially accelerating development cycles and reducing errors in complex analog circuit creation.

GNN-ACLP: Graph Neural Networks based Analog Circuit Link Prediction