Overlapping community detection for brain networks

🔗📕 Paper: Benchmarking overlapping community detection methods for applications in human connectomics

I made a video walking through the background, results, and interpretation of this paper, check it out below on YouTube! </center>

Developing a data-driven method for tailoring overlapping community detection algorithms (OCDAs) to empirical networks

Figure 1 from Bryant, Jha, et al. (2026): Benchmarking overlapping community detection methods for applications in human connectomics.

Figure 2 from Bryant, Jha, et al. (2026): Benchmarking overlapping community detection methods for applications in human connectomics.

The connectome-tailored benchmark network ensemble highlights OSLOM as the top-performing algorithm

Figure 3 from Bryant, Jha, et al. (2026): Benchmarking overlapping community detection methods for applications in human connectomics.

Overlapping community detection reveals regions that bridge multiple networks and sit high in the cortical hierarchy

Figure 4 from Bryant, Jha, et al. (2026): Benchmarking overlapping community detection methods for applications in human connectomics.

Figure 5 from Bryant, Jha, et al. (2026): Benchmarking overlapping community detection methods for applications in human connectomics.

Comparing OSLOM with Louvain demonstrates the importance of allowing network-bridging overlaps

Figure 6 from Bryant, Jha, et al. (2026): Benchmarking overlapping community detection methods for applications in human connectomics.