no code implementations • 25 Jul 2023 • Andrea Della Vecchia, Kibidi Neocosmos, Daniel B. Larremore, Cristopher Moore, Caterina De Bacco
We present a physics-inspired method for inferring dynamic rankings in directed temporal networks - networks in which each directed and timestamped edge reflects the outcome and timing of a pairwise interaction.
1 code implementation • 22 Jan 2020 • Tzu-Chi Yen, Daniel B. Larremore
In bipartite networks, community structures are restricted to being disassortative, in that nodes of one type are grouped according to common patterns of connection with nodes of the other type.
1 code implementation • 3 Sep 2017 • Caterina De Bacco, Daniel B. Larremore, Cristopher Moore
We present a physically-inspired model and an efficient algorithm to infer hierarchical rankings of nodes in directed networks.
1 code implementation • 5 Jan 2017 • Caterina De Bacco, Eleanor A. Power, Daniel B. Larremore, Cristopher Moore
In particular, this allows us to bundle layers together to compress redundant information, and identify small groups of layers which suffice to predict the remaining layers accurately.
Social and Information Networks Statistical Mechanics Physics and Society
no code implementations • 20 Aug 2016 • Leto Peel, Daniel B. Larremore, Aaron Clauset
We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models.
1 code implementation • 1 Aug 2016 • Bailey K. Fosdick, Daniel B. Larremore, Joel Nishimura, Johan Ugander
We place particular emphasis on the importance of specifying the appropriate graph labeling (stub-labeled or vertex-labeled) under which to consider a null model, a choice that closely connects the study of random graphs to the study of random contingency tables.
Methodology Social and Information Networks Data Analysis, Statistics and Probability Physics and Society Quantitative Methods
1 code implementation • 7 Apr 2016 • Andrew Berdahl, Christa Brelsford, Caterina De Bacco, Marion Dumas, Vanessa Ferdinand, Joshua A. Grochow, Laurent Hébert-Dufresne, Yoav Kallus, Christopher P. Kempes, Artemy Kolchinsky, Daniel B. Larremore, Eric Libby, Eleanor A. Power, Caitlin A. Stern, Brendan Tracey
Third, in the context of dynamic social networks, we find that preferences for increased global infection accelerate spread and produce superexponential fixation, but preferences for local assortativity halt epidemics by disconnecting the infected from the susceptible.
Physics and Society Multiagent Systems Social and Information Networks Adaptation and Self-Organizing Systems Populations and Evolution
1 code implementation • 12 Mar 2014 • Daniel B. Larremore, Aaron Clauset, Abigail Z. Jacobs
Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected.