Remco van der Hofstad
Eindhoven University of Technology
328 Papers
2K Citations
Remco van der Hofstad is an academic researcher from Eindhoven University of Technology. The author has contributed to research in topics: Random graph & Random walk. The author has an hindex of 43, co-authored 304 publications. Previous affiliations of Remco van der Hofstad include McMaster University & Delft University of Technology.
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Papers
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Backbone scaling limit of the high-dimensional IIC: extended version
TL;DR: In this article, the scaling limit of the backbone of the high-dimensional incipient infinite cluster (IIC) was identified both in the finite and the long-range setting.
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Cliques in rank-1 random graphs: The role of inhomogeneity
TL;DR: In this article, the authors studied the asymptotic behavior of clique number in rank-1 inhomogeneous random graphs, where edge probabilities between vertices are roughly proportional to the product of their vertex weights.
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Large Deviations of Bivariate Gaussian Extrema
TL;DR: In this article, the authors established sharp tail asymptotics for componentwise extreme values of bivariate Gaussian random vectors with arbitrary correlation between the components, and demonstrated the existence of a restricted large deviations principle and identified the unique rate function associated with these asmptotics.
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Personalized PageRank with Node-dependent Restart
TL;DR: This work introduces two generalizations of Personalized PageRank with node-dependent restart and shows that both generalizations have an elegant expression connecting the so-called direct and reverse PersonalizedPageRank that yield a symmetry property of these Personalization PageRanks.
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The survival probability and r-point functions in high dimensions
TL;DR: In this article, it was shown that if the r-point functions scale to those of the canonical measure of super-Brownian motion, and if a certain self-repellence condition is satisfied, then n\theta_n\ra 2/(AV), where A is the asymptotic expected number of particles alive at time n, and V is the vertex factor of the model.
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