TimeGraph: Synthetic Generation of Graph Sequences for Realistic Mobile Connectivity Models

Softwarized networking solutions are a key enabler for effective and efficient communications in natural disaster recovery scenarios. However, the design development of reliable and robust softwarization solutions in this context is hampered by the scarcity of reference datasets which accurately capture the real-world behavior - and variability - of those environments. This paper presents a method for synthetic generation of sequences of graphs using state-of-the-art Graph Neural Networks (GNNs) and Time-series Generative Adversarial Networks (TimeGAN). By leveraging available real-world data, the proposed approach generates synthetic datasets that closely replicate the features and connectivity patterns found in actual scenarios. These synthetic datasets not only support the training of AI models but also enable testing and evaluation of solutions across different but similar scenarios. Preliminary results using the Anglova scenario show that our solution accurately captures spatio-temporal behaviour in disrupted networks, making it a powerful tool for developing and validating systems in fields where access to real-world data is limited, enhancing their generalizability and reliability.