<ul><li>Mobile networks consist of interconnected radio nodes strategically positioned across various geographical regions to provide connectivity services.</li><li>In this work, graph-based deep learning methods are used to determine mobility relations in mobile networks, trained on radio node configuration data and Automatic Neighbor Relations (ANR).</li><li>The evaluation of two deep learning models, graph neural network (GNN) model and multilayer perceptron, showed the effectiveness of considering graph structure in improving results.</li><li>The use of heuristics based on the distance between radio nodes was also investigated, which significantly improved precision and accuracy.</li></ul>

Deep Learning on Graphs for Mobile Network Topology Generation

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