Graph Neural Networks (GNN) have been recently proposed to learn, model and generalize over graph structured data. Computer Networks are fundamentally graphs, and many of its relevant characteristics -such as topology and routing- are represented as graph-structured data. In this context, GNN are a central tool to apply ML techniques to Computer Networks. GNN can learn the relationship of complex network characteristics and build relevant models that can be useful to plan and manage the network. In addition and in combination with Deep-Reinforcement Learning (DRL) techniques, GNN can help developing autonomous network optimization mechanisms that result in unprecedented performance, achieving the ultimate vision of self-driving networks.

The Barcelona Neural Networking Center (BNN-UPC) has been created as an initiative of Prof. Albert Cabellos and Prof. Pere Barlet at UPC (Universitat Politècnica de Catalunya) with the main goals of carrying fundamental research in the field of Graph Neural Network applied to Computer Networks, and educating and training the new generation of students.