Search Results for author: Daniel B. Larremore

Found 8 papers, 6 papers with code

A model for efficient dynamical ranking in networks

no code implementations25 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.

Community Detection in Bipartite Networks with Stochastic Blockmodels

1 code implementation22 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.

Community Detection Stochastic Block Model

A physical model for efficient ranking in networks

1 code implementation3 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.

Community detection, link prediction, and layer interdependence in multilayer networks

1 code implementation5 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

The ground truth about metadata and community detection in networks

no code implementations20 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.

Community Detection

Configuring Random Graph Models with Fixed Degree Sequences

1 code implementation1 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

Dynamics of beneficial epidemics

1 code implementation7 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

Efficiently inferring community structure in bipartite networks

1 code implementation12 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.

Community Detection Stochastic Block Model

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