Synthesizing and Scaling WAN Topologies Using Permutation-Invariant Graph Generative Models

Real-world Wide Area Network (WAN) topologies are scarce. The shift towards machine learning in network management and optimization brings a need for large datasets, including real-world topologies. WAN topologies can be generated using graph generative models. Graph generative models can be divided into parameterized and data-driven approaches. Data-driven approaches can be further divided into permutation-invariant and permutation-variant. In this paper, we improve on existing work, which utilized adjacency-matrix-based, permutation-variant Generative Adversarial Networks to synthesize WAN topologies. We achieve this by using existing, data-driven approaches that are permutation-invariant w.r.t. their input. Our results show a decrease in the mean Kolmogorov-Smirnov distance over various graph theoretical metrics of 80 %. Furthermore, we employ graph upscaling models to increase WAN topology sizes while preserving their properties up to a scaling factor of 256. We publish all datasets and hope they can be of help in training machine learning models, such as communication network performance prediction models or digital twins, enabling better automated network management.