19 citations · 34 across the 9 of their papers we have counts for
10 papers
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…
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…
Planted clique recovery in random geometric graphs
Konstantin Avrachenkov, Andrei Bobu, Nelly Litvak +1
We investigate the problem of identifying planted cliques in random geometric graphs, focusing on two distinct algorithmic approaches: the first based on vertex degrees (VD) and th…
Optimal network geometry detection for weak geometry
R. Michielan, C. Stegehuis
Network geometry, characterized by nodes with associated latent variables, is a fundamental feature of real-world networks. Still, when only the network edges are given, it may be…
Homophily Within and Across Groups
Abbas K. Rizi, Riccardo Michielan, Clara Stegehuis +1
Homophily -- the tendency of individuals to interact with similar others -- shapes how networks form and function. Yet existing approaches typically collapse homophily to a single…
Optimal subgraphs in geometric scale-free random graphs
Riccardo Michielan, Clara Stegehuis, Matthias Walter
Geometric scale-free random graphs are popular models for networks that exhibit as heavy-tailed degree distributions, small-worldness and high clustering. In these models, vertices…
Geometric random intersection graphs with general connection probabilities
Maria Deijfen, Riccardo Michielan
Let and be the point sets of two independent homogeneous Poisson processes on . A graph with vertex set $\mathca…
Localized geometry detection in scale-free random graphs
Gianmarco Bet, Riccardo Michielan, Clara Stegehuis
We consider the problem of detecting whether a power-law inhomogeneous random graph contains a geometric community, and we frame this as an hypothesis testing problem. More precise…
Detecting hyperbolic geometry in networks: why triangles are not enough
Riccardo Michielan, Nelly Litvak, Clara Stegehuis
In the past decade, geometric network models have received vast attention in the literature. These models formalize the natural idea that similar vertices are likely to connect. Be…
Cliques in geometric inhomogeneous random graph
Riccardo Michielan, Clara Stegehuis
Many real-world networks were found to be highly clustered, and contain a large amount of small cliques. We here investigate the number of cliques of any size k contained in a geom…