45 citations · 121 across the 13 of their papers we have counts for
14 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…
Leaves of preferential attachment trees
Harrison Hartle, P. L. Krapivsky
We provide a local probabilistic description of the limiting statistics of large preferential attachment trees in terms of the ordinary degree (number of neighbors) but augmented w…
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…
Statistics of leaves in growing random trees
Harrison Hartle, P. L. Krapivsky
Leaves, i.e., vertices of degree one, can play a significant role in graph structure, especially in sparsely connected settings in which leaves often constitute the largest fractio…
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…
The Metric Dimension of Sparse Random Graphs
Josep Díaz, Harrison Hartle, Cristopher Moore
In 2013, Bollobás, Mitsche, and Pralat at gave upper and lower bounds for the likely metric dimension of random Erdős-Rényi graphs for a large range of expected degrees $d…
Work extraction with feedback control using limited resources
Harrison Hartle, David Wolpert, Andrew Stier +2
Many physical, biological, and even social systems are faced with the problem of how to efficiently harvest free energy from an environment that can have many possible states, yet…
Spatial scales of COVID-19 transmission in Mexico
Brennan Klein, Harrison Hartle, Munik Shrestha +13
During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay. This hie…
Dynamics of cold random hyperbolic graphs with link persistence
Sofoclis Zambirinis, Harrison Hartle, Fragkiskos Papadopoulos
We consider and analyze a dynamic model of random hyperbolic graphs with link persistence. In the model, both connections and disconnections can be propagated from the current to t…
Entropy of labeled versus unlabeled networks
Jeremy Paton, Harrison Hartle, Huck Stepanyants +2
The structure of a network is an unlabeled graph, yet graphs in most models of complex networks are labeled by meaningless random integers. Is the associated labeling noise always…
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…
netrd: A library for network reconstruction and graph distances
Stefan McCabe, Leo Torres, Timothy LaRock +4
Over the last two decades, alongside the increased availability of large network datasets, we have witnessed the rapid rise of network science. For many systems, however, the data…
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…
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…