7 papers
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…
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…
Multiplexity amplifies geometry in networks
Jasper van der Kolk, Dmitri Krioukov, Marián Boguñá +1
Many real-world network are multilayer, with nontrivial correlations across layers. Here we show that these correlations amplify geometry in networks. We focus on mutual clustering…
Deterministic construction of typical networks in network models
Narayan G. Sabhahit, Moritz Laber, Harrison Hartle +4
It is often desirable to assess how well a given dataset is described by a given model. In network science, for instance, one often wants to say that a given real-world network app…
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…
Local d'Alembertian for causal sets
Marián Boguñá, Dmitri Krioukov
Causal set theory is an intrinsically nonlocal approach to quantum gravity, inheriting its nonlocality from Lorentzian nonlocality. This nonlocality causes problems in defining dif…