1.1k citations · 6.7k across the 76 of their papers we have counts for
78 papers
Sampling biases in sprinkled causal sets
Marián Boguñá, Dmitri Krioukov
Poisson sprinkling provides a Lorentz-invariant way of generating causal sets from a continuum spacetime, but numerical work necessarily restricts the sprinkling to a finite region…
Percolation in the Stochastic Block Model
Luca Pieleanu, Moritz Laber, Narayan G. Sabhahit +1
The stochastic block model is a paradigmatic model of networks with community structure. Yet percolation in the model has been studied primarily in cases with a fixed block structu…
The rank and layer distributions in random recursive trees
Huck Stepanyants, P. L. Krapivsky, Harrison Hartle +1
The distribution of node depths in a network is crucial for analyzing network structure. Two measures, rank and layer, quantify how deep inside a network a node is. The rank is the…
Deterministic construction of typical networks in network models
Narayan G. Sabhahit, Moritz Laber, Harrison Hartle +4
In network science, one often wants to say that a given real-world network appears to come from a particular network model. In statistical physics, the corresponding problem is abo…
Projective limits of probabilistic symmetries and their applications to random graph limits
Pim van der Hoorn, Huck Stepanyants, Dmitri Krioukov
We couple projective limits of probability measures to direct limits of their symmetry groups. We show that the direct limit group is the group of symmetries of the projective limi…
Growing unlabeled networks
Harrison Hartle, Brennan Klein, Dmitri Krioukov +1
Models of growing networks are a central topic in network science. In these models, vertices are usually labeled by their arrival time, distinguishing even those node pairs whose s…