C
Clara Stegehuis
Researcher at University of Twente
Publications - 62
Citations - 656
Clara Stegehuis is an academic researcher from University of Twente. The author has contributed to research in topics: Random graph & Degree (graph theory). The author has an hindex of 10, co-authored 53 publications receiving 483 citations. Previous affiliations of Clara Stegehuis include Eindhoven University of Technology.
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Robust subgraph counting with distribution-free random graph analysis
TL;DR: In this article, the authors develop robust subgraph counts that do not depend on the entire degree distribution, but only on the mean and mean absolute deviation (MAD), summary statistics that are easy to obtain for most real-world networks.
Localized geometry detection in scale-free random graphs
TL;DR: In this paper , the authors consider the problem of detecting whether a power-law inhomogeneous random graph contains a geometric community, and frame this as an hypothesis testing problem, assuming that we are given a sample from an unknown distribution on the space of graphs on n vertices.
Journal ArticleDOI
Limit theorems for assortativity and clustering in null models for scale-free networks
TL;DR: The erased configuration model as discussed by the authors is obtained when self-loops and multiple edges in the configuration model are removed, and an upper bound for the number of such erased edges for regularly-varying degree distributions with infinite variance is established.
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Contact tracing in configuration models
Ivan Kryven,Clara Stegehuis +1 more
TL;DR: In this article, the authors study how the final size of an epidemic is influenced by the procedure that combines contact tracing and quarantining on a network null model: the configuration model, where infected vertices may self-quarantine and trace their infector with a given success probability.
Book ChapterDOI
Finding induced subgraphs in scale-free inhomogeneous random graphs
TL;DR: In this paper, the authors studied the problem of finding a copy of a specific induced subgraph on inhomogeneous random graphs with infinite variance power-law degrees and provided a fast algorithm for the problem.