An R package for the implementation of the "Smith-Pittman" (2024) community detection algorithm. Also known as the node degree+edge betweenness algorithm. Compatible with the igraph ecosystem.

• For the Python implementation, see ig_degree_betweenness_py.
• For the C implementation, see ig_degree_betweenness_c

Algorithm Visualizations

How the Smith-Pittman algorithm works:

Installing this package

To install the stable release of this package from CRAN run:

install.packages("ig.degree.betweenness")

To install the development version of this package run:

# install.packages("devtools")
devtools::install_github("benyamindsmith/ig.degree.betweenness")

Sample Usage

Applying the node degree+edge betweenness algorithm can be done by making use of the cluster_degree_betweenness().

An example of using the code is:

library(igraphdata)
library(ig.degree.betweenness)

data("karate")

sp <- cluster_degree_betweenness(karate)
plot(
sp,
karate,
main= "Node degree+edge betweenness clustering"
)

Citation

To cite package ‘ig.degree.betweenness’ in publications use:

Smith B, Pittman T, Xu W (2024). “Centrality in Collaboration: community detection for oncology researchers.” University of Toronto Journal of Public Health, 5(1). doi:10.33137/utjph.v5i1.44130

Smith B, Pittman T, Xu W (2026). “Detecting communities when order and direction matter in social network analysis.” Canadian Journal of Statistics, n/a(n/a), e70060. doi:10.1002/cjs.70060

A BibTeX entry for LaTeX users is

  @Article{Smith_Pittman_Xu_2024,
    title = {Centrality in Collaboration: community detection for oncology researchers},
    author = {Benjamin Smith and Tyler Pittman and Wei Xu},
    journal = {University of Toronto Journal of Public Health},
    volume = {5},
    number = {1},
    year = {2024},
    month = {nov},
    doi = {10.33137/utjph.v5i1.44130},
    url = {https://utjph.com/index.php/utjph/article/view/44130},
  }
  
   @Article{Smith_Pittman_Xu_2026,
    title = {Detecting communities when order and direction matter in social network analysis},
    author = {Benjamin Smith and Tyler Pittman and Wei Xu},
    journal = {Canadian Journal of Statistics},
    volume = {n/a},
    number = {n/a},
    pages = {e70060},
    year = {2026},
    doi = {https://doi.org/10.1002/cjs.70060},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cjs.70060},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjs.70060},
    keywords = {Community detection, directed networks, edge betweenness, modularity, node degree},
  }