Mobile cellular networks are widely integrated in today’s infrastructure. These networks are constantly evolving and continuously expanding, especially with the introduction of fifth-generation (5G). It is important to ensure the effectiveness of these expansions.Mobile networks consist of a set of radio nodes that are distributed in a geographicalregion to provide connectivity services. Each radio node is served by a set of cells. Thehandover relations between cells is determined by Software features such as AutomaticNeighbor Relations (ANR). The handover relations, also refereed as edges, betweenradio nodes in the mobile network graph are created through historical interactions between User Equipment (UE) and radio nodes. The method has the limitation of not being able to set the edges before the physical hardware is integrated. In this work, we usegraph-based deep learning methods to determine mobility relations (edges), trained onradio node configuration data and a set of reliable relations of ANR in stable networks.The report focuses on measuring the accuracy and precision of different graph baseddeep learning approaches applied to real-world mobile networks. The report considers four models. Our comprehensive experiments on Telecom datasets obtained fromoperational Telecom Networks demonstrate that graph neural network model and multilayer perceptron trained with Binary Cross Entropy (BCE) loss outperform all othermodels. The four models evaluation showed that considering graph structure improveresults. Additionally, the model investigates the use of heuristics to reduce the trainingtime based on distance between radio node to eliminate irrelevant cases. The use ofthese heuristics improved precision and accuracy.