The quality of components produced through additive manufacturing processes, such as selective laser melting (SLM), is significantly influenced by heat transfer phenomena. Numerical simulations have emerged as valuable tools for gaining a deeper understanding of these processes. Deep investigation is made possible by a large amount of sensor data in this area. Both offers the potential to reduce the cost and time associated with empirical experimentation. Physics-informed neural networks (PINNs) combine the data-driven capabilities of deep neural networks with the mathematical formulations of physical laws, such as heat diffusion. In particular, the gap between numerical simulations and data observations can be bridged. In this paper, we present a novel neural diffusion graph convolutional network (NDGCN) designed to reveal physically interpretable parameters and accurately predict heat transfer dynamics during the SLM process. Our methodology involves representing the fabricated part as a graph model, constructed from high-dimensional data. This facilitates the integration of complex geometries and thermal properties into our predictive framework.