The C implementation of the "Smith-Pittman" algorithm. Also known as the node degree+edge betweenness community detection algorithm. Uses the igraph C library.

Why use the C implementation? Because it executes faster.

R version of ig.degree.betweenness can be installed from CRAN or GitHub.

Python version of ig.degree.betweenness can be installed from PyPi or GitHub.

Installation

The instructions below have been presently tested to work on Windows operating systems with the MingW64 Command Line Interface and with CMake installed.

1. Install igraph C following the instructions at igraph Reference Manual for using the C library

2. Compile the code by running:

make clean
make

Execution

To run the compiled graph clustering executable on an input edge list in NCOL format that is tab separated (see simulated dataset of therapies_edgelist.txt) to obtain output of the node degree+edge betweenness community detection algorithm.

The compiled code is meant to work with both directed and undirected graphs.

Undirected Graphs

./bin/cluster_degree_betweenness.exe  <path_to_edgelist>.txt

Output

Directed Graphs

./bin/cluster_degree_betweenness.exe -directed <path_to_edgelist>.txt

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},
  }