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This paper analyzes distance-preserving embeddings in inhomogeneous random graphs, providing tighter distortion bounds than classical worst-case results and introducing a GNN-augmented variant that learns universal features from small graphs.
Amazon discusses the evolution of flat datacenter network topologies, from theoretical expander graphs to practical implementations like VL2 and Jellyfish, and current research into Penrose tiling-based designs at AWS.