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cs.DS2026
Efficient generation of expected-degree graphs via edge-arrivals
Gianlorenzo D'Angelo, Riccardo Michielan
We study the efficient generation of random graphs with a prescribed expected degree sequence, focusing on rank-1 inhomogeneous models in which vertices are assigned weights and ed…
math.PR2026
Large deviations for subgraphs in inhomogeneous random graphs
Riccardo Michielan, Clara Stegehuis, Bert Zwart
Inhomogeneous random graphs are fundamental models for real-world networks, where prescribed degrees are imposed as soft constraints. A common assumption in such models is that the…