The fault diagnosis of rolling bearings is of utmost importance in industrial applications to ensure mechanical systems' reliability, safety, and economic viability. However, conventional data-driven fault diagnosis techniques mainly depend on a pre-existing dataset with complete failure modes and knowledge to serve as the training data, which may not be available or accessible in some crucial industrial scenarios. This can limit the practicality of these methodologies in real-world industrial applications. This paper addresses this issue by developing a novel digital twin-enabled domain adversarial graph network (DT-DAGN). The main contributions of this paper are as follows: 1) the development of a comprehensive and accurate digital twin model for rolling bearings that includes a dynamic simulation of the bearing's operational status using only its structural parameters and failure severity/size to obtain the system's vibration response, and 2) the development of a novel graph convolutional network-based transfer learning framework to transfer knowledge from simulated datasets to measured datasets, enabling effective fault diagnostics of bearings with limited knowledge. A series of experiments are applied to validate the efficacy of the developed methodology.