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20192026
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physics.soc-ph2025

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…

physics.soc-ph2025

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…

physics.soc-ph2021

Dynamic Hidden-Variable Network Models

Harrison Hartle, Fragkiskos Papadopoulos, Dmitri Krioukov

Models of complex networks often incorporate node-intrinsic properties abstracted as hidden variables. The probability of connections in the network is then a function of these var…

physics.soc-ph2020

Network comparison and the within-ensemble graph distance

Harrison Hartle, Brennan Klein, Stefan McCabe +4

Quantifying the differences between networks is a challenging and ever-present problem in network science. In recent years a multitude of diverse, ad hoc solutions to this problem…

physics.soc-ph2019

Classical Information Theory of Networks

Filippo Radicchi, Dmitri Krioukov, Harrison Hartle +1

Existing information-theoretic frameworks based on maximum entropy network ensembles are not able to explain the emergence of heterogeneity in complex networks. Here, we fill this…