Data-driven Intra-Autonomous Systems Graph Generator

arXiv:2308.05254v2 Announce Type: replace-cross
Abstract: Accurate modeling of realistic network topologies is essential for evaluating novel Internet solutions. Current topology generators, notably scale-free-based models, fail to capture multiple properties of intra-AS topologies. While scale-free networks encode node-degree distribution, they overlook crucial graph properties like betweenness, clustering, and assortativity. The limitations of existing generators pose challenges for training and evaluating deep learning models in communication networks, emphasizing the need for advanced topology generators encompassing diverse Internet topology characteristics. This paper introduces …

abstract arxiv autonomous autonomous systems challenges clustering cs.lg cs.ni current data data-driven distribution encode free generator generators graph internet limitations modeling multiple network networks node novel scale solutions systems topology type