Official code: lorenzhs/wrs (21 stars, GPL-3.0, last commit 2020-12-09).
R-MAT is a simple, widely used recursive model for generating `complex network' graphs with a power law degree distribution and community structure. We make R-MAT even more useful by reducing the required work per edge from logarithmic to constant. The algorithm works in an embarrassingly parallel way.
Artificial Benchmark for Community Detection (ABCD): Fast Random Graph Model with Community Structure
Analytical Formulation of the Block-Constrained Configuration Model
Communication-Free Distributed GNN Training with Vertex Cut
Learning on Large Graphs using Intersecting Communities
Generating Bayesian Network Models from Data Using Tsetlin Machines
Learning Morphisms with Gauss-Newton Approximation for Growing Networks
@article{hbschleschneider2019linear,
title = {Linear Work Generation of R-MAT Graphs},
author = {Lorenz Hübschle-Schneider and Peter Sanders},
year = {2019},
eprint = {1905.03525},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/1905.03525}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/1905.03525)