C Greenhill - arXiv preprint arXiv:2201.04888, 2022 - arxiv.org
Graphs are used in many disciplines to model the relationships that exist between objects in a complex discrete system. Researchers may wish to compare a network of interest to a" …
Uniform sampling of binary matrix with fixed margins is an important and difficult problem in statistics, computer science, ecology and so on. The well-known swap algorithm would be …
G Amanatidis, P Kleer - Proceedings of the Thirtieth Annual ACM-SIAM …, 2019 - SIAM
The switch Markov chain has been extensively studied as the most natural Markov Chain Monte Carlo approach for sampling graphs with prescribed degree sequences. We use …
G Amanatidis, P Kleer - Random Structures & Algorithms, 2020 - Wiley Online Library
The switch Markov chain has been extensively studied as the most natural Markov chain Monte Carlo approach for sampling graphs with prescribed degree sequences. We show …
K Godard, ZP Neal - Journal of Complex Networks, 2022 - academic.oup.com
Many applications require randomly sampling bipartite graphs with fixed degrees or randomly sampling incidence matrices with fixed row and column sums. Although several …
CJ Carstens, P Kleer - Approximation, Randomization, and …, 2018 - drops.dagstuhl.de
We consider the well-studied problem of uniformly sampling (bipartite) graphs with a given degree sequence, or equivalently, the uniform sampling of binary matrices with fixed row …
D Allendorf, U Meyer, M Penschuck, H Tran - Journal of Parallel and …, 2023 - Elsevier
The uniform sampling of simple graphs matching a prescribed degree sequence is an important tool in network science, eg to construct graph generators or null-models. Here, the …
The switch Markov chain has been extensively studied as the most natural Markov chain Monte Carlo approach for sampling graphs with prescribed degree sequences. In this work …
The approximate uniform sampling of graphs with a given degree sequence is a well-known, extensively studied problem in theoretical computer science and has significant …