Heuristic and exact modularity optimization with size-constrained communities
For network scientists and practitioners needing communities of specific sizes, this provides a principled alternative to ad-hoc resolution parameter tuning.
The paper tackles size-constrained community detection in networks, proposing a heuristic for modularity optimization under community size constraints and an exact integer optimization model as a baseline. Results on synthetic and real networks show the proposed methods outperform the common practice of adjusting resolution parameters.
When searching for communities in networks, domain experts may have some prior expectations about the size of communities. Yet, community detection methods normally do not optimize communities under cluster size constraints. Multi-resolution techniques allow users to indirectly control the average community size through changing a resolution parameter, but this practice does not control the size of individual communities. We here study the problem of size-constrained community detection, where the size of all communities is limited to a user-specified range of values, in the context of modularity optimization. We propose a heuristic for modularity optimization under community size constraints. To demonstrate the reliability of our proposed heuristic, we also formulate an exact integer optimization model and use its results as a baseline. Our analysis based on synthetic benchmarks and real networks demonstrate the issues with the currently common practice of changing resolution parameters and reveal the advantages of the proposed methods as a principled way of obtaining size-constrained communities. The proposed method is publicly available in the Python Leiden algorithm package.