Modularity score comparison between different algorithms in network analysis
Since the goal of modularity estimation algorithms is to find the community structure that maximizes modularity, it is valid to compare different algorithms based on their modularity estimate. However, i) it is not the only consideration for choosing a community detection algorithm, and ii) the obtained modularity estimate will depend on the particular structure of each network, e.g. its number of nodes. For a principled comparison between several different algorithms, see Yang et al. (2016). Based on multiple criteria, they identify the multilevel (or Louvain) algorithm (Blondel et al. 2008) as the overall best choice.




References
- Yang, Zhao, René Algesheimer, and Claudio J. Tessone. “A Comparative Analysis of Community Detection Algorithms on Artificial Networks.” Scientific Reports 6, no. 1 (August 2016). https://doi.org/10.1038/srep30750.
- Blondel, Vincent D., Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. “Fast Unfolding of Communities in Large Networks.” Journal of Statistical Mechanics: Theory and Experiment 2008, no. 10 (October 2008): P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008.