The third Party (3PP) IPs have been widely used in industry to facilitate hardware designs. However, it has been a major concern that hardware Trojan may be embedded in 3PP IPs. It is important to detect any Trojans in early design stages. This project studies graphic neural networks (GNN) based methods for hardware Trojan detection for large complex 3PP IPs. The GNN models are trained with individual components and used for detecting hardware Trojans in large complex systems. In addition, we will study the techniques that partition large graphs and make the GNN based detection methods more accessible.