Digital twin opportunities with leveraging graph neural networks on real network data

Sixth-generation networks propose integrating multiple networks while ensuring seamless network performance. Hence, networks are becoming increasingly complex while the traditional methods to manage networks are facing significant challenges as the topology sizes, traffic patterns, and network domains are changing. Autonomous network management solutions, which are often built on digital twins, are emerging as possible candidates for addressing these challenges. Machine learning models are widely used for realizing digital twins. Among many neural network structures, graph neural networks are a subclass of promising machine learning methods that perform well in graph-structured data such as network topologies. In this paper, we explore GNN performance on real network data and present our solution to per-flow mean delay prediction which achieves a MAPE of 35.39%, improving the baseline solutions by over 20% together with additional findings and further improved models for Graph Neural Networking Challenge 2023.