Benchmarking overlapping community detection methods for applications in human connectomics
Annie G Bryant1,2, Aditi Jha3, Sumeet Agarwal4
1School of Physics, The University of Sydney, Camperdown, NSW, Australia.
Network Neuroscience (Cambridge, Mass.)
|January 15, 2026
Summary
Selecting the best brain network analysis method is crucial. We developed a data-driven approach using benchmark networks to choose overlapping community detection algorithms (OCDAs), identifying key brain regions.
Area of Science:
- Neuroscience
- Network Science
- Computational Biology
Background:
- Brain networks display modular organization for specialized functions.
- Traditional methods fail to identify multifunctional brain regions spanning multiple modules.
- Overlapping community detection algorithms (OCDAs) exist, but selecting the best one is challenging.
Purpose of the Study:
- To introduce a data-driven method for selecting the optimal OCDA and parameters.
- To objectively evaluate OCDAs using a benchmark network ensemble.
- To analyze the human right-hemisphere structural connectome using the selected OCDA.
Main Methods:
- Generated a tailored ensemble of benchmark networks.
- Assessed 22 unique OCDAs and parameter settings.
- Applied the best-performing OCDA (OSLOM) to the human right-hemisphere structural connectome.
Main Results:
- The order statistics local optimization method (OSLOM) excelled in identifying overlapping structures.
- OSLOM revealed a seven-network decomposition of the right-hemisphere cortex.
- Fifteen overlapping regions bridge these modules, potentially indicating higher-order functions and increasing network participation along the cortical hierarchy.
Conclusions:
- A data-driven method for OCDA selection enhances the analysis of complex networks.
- The findings highlight the importance of overlapping community structure in brain networks.
- This approach offers new ways to detect and quantify informative structures in real-world complex systems.
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