Abstract: The reconstruction of charged-particle trajectories, ranking amongst the most computationally demanding tasks in particle collider experiments, such as the ATLAS experiment at CERN, plays an essential role in any High-Energy Physics program, as it determines the quality of particle identification, kinematic measurement, vertex finding, lepton reconstruction, jet flavor tagging, and other downstream tasks. The upcoming High Luminosity phase of the Large Hadron Collider (HL-LHC) represents a steep increase in the average number of proton-proton interactions and hence in the computing resources required for offline track reconstruction of the ATLAS Inner Tracker (ITk). As such, track pattern recognition algorithms based on Graph Neural Networks (GNNs) have been demonstrated as a promising approach to these challenges. We present a novel algorithm developed for track reconstruction in silicon detectors based on a number of deep learning techniques including GNN architectures. Using detector simulation of collision events associated with the production of a top quark pair on the latest version of ITk geometry under HL-LHC conditions, we demonstrate the performance of our algorithm, and compare to that of the tracking algorithm currently used in ATLAS on a range of important physics metrics, including reconstruction efficiency, and track parameter resolution. Finally, we discuss the algorithm's computational performance and optimisations that reduce computing costs, as well as our effort to integrate into the ATLAS analysis software for full-chain testing and production.