activity
20042012
most citedDiameters in preferential attachment models

111 citations · 350 across the 13 of their papers we have counts for

collaborators

13 papers

math.PR2012★ 4 cited

Universality for first passage percolation on sparse random graphs

Shankar Bhamidi, Remco van der Hofstad, Gerard Hooghiemstra

We consider first passage percolation on sparse random graphs with prescribed degree distributions and general independent and identically distributed edge weights assumed to have…

math.PR2011

Scale-free percolation

Maria Deijfen, Remco van der Hofstad, Gerard Hooghiemstra

We formulate and study a model for inhomogeneous long-range percolation on $\Zbold^d$. Each vertex $x\in\Zbold^d$ is assigned a non-negative weight , where $(W_x)_{x\in\Zbold^…

math.PR2010★ 7 cited

Weak disorder in the stochastic mean-field model of distance II

Shankar Bhamidi, Remco van der Hofstad, Gerard Hooghiemstra

In this paper, we study the complete graph with n vertices, where we attach an independent and identically distributed (i.i.d.) weight to each of the n(n-1)/2 edges. We focus…

math.PR2010★ 1 cited

First passage percolation on the Erdős-Rényi random graph

Shankar Bhamidi, Remco van der Hofstad, Gerard Hooghiemstra

In this paper we explore first passage percolation (FPP) on the Erdős-Rényi random graph , where each edge is given an independent exponential edge weight with rate 1. In…

math.PR2009

Extreme value theory, Poisson-Dirichlet distributions and FPP on random networks

Shankar Bhamidi, Remco van der Hofstad, Gerard Hooghiemstra

We study first passage percolation on the configuration model (CM) having power-law degrees with exponent . To this end we equip the edges with exponential weights. We…

math.PR2009★ 66 cited

First passage percolation on random graphs with finite mean degrees

Shankar Bhamidi, Remco van der Hofstad, Gerard Hooghiemstra

We study first passage percolation on the configuration model. Assuming that each edge has an independent exponentially distributed edge weight, we derive explicit distributional a…