Type-Aware Heterogeneous Graph Network for Circuit Link Prediction

Link prediction for circuit completion is a crucial task in Electronic Design Automation (EDA), aiming to infer missing connections in incomplete circuits to accelerate design workflows and ensure Printed Circuit Board (PCB) reliability. However, due to the practical reality that circuit connectivity is defined by nets that connect specific pins of component instances, existing methods that rely on homogeneous representations and scalar shortest path distances collapse pin-level constraints into a single proximity signal. This collapse obscures component identities and pin semantics, and fails to capture long-range functional paths, which can slow design closure and potentially compromise PCB reliability by increasing the risk of electrically invalid links and subsequent rework. Addressing these challenges, we present a T ype-aware H eterogeneous G raph network for C ircuit link prediction ( THGC ), which enables accurate circuit link prediction by explicitly preserving component functional semantics and modeling type-aware dependencies. Specifically, THGC constructs a circuit-aware heterogeneous graph from netlists to preserve physical connectivity and component semantics, and refines representations through a multi-view encoder with Type-Aware Distance Encoding (TADE) to capture non-local dependencies. It then performs circuit-oriented link prediction using a heterogeneous graph network with dense convolutions and global sort pooling, optimized by a composite objective enforcing topological and semantic consistency, to predict electrically feasible missing connections for circuit completion. Comprehensive experiments are performed on 6,195 circuits collected from three benchmarks, and the results demonstrate that THGC consistently outperforms state-of-the-art methods, achieving up to a 47.51% relative improvement in terms of AUC. These results confirm that THGC effectively preserves component functional semantics and captures type-aware non-local dependencies for accurate circuit link prediction. The source code is available at https://anonymous.4open.science/r/THGC.