Network Community Detection in Connectomics Data using Graph Theory

This work presents a comprehensive descriptive analysis of a complex network derived from individual neuronal reconstructions from the vinegar fly (Drosophila melanogaster) using graph theory. The primary aim was to establish an informed criterion for selecting the most suitable community detection algorithm based on the network’s topology. We implemented four state-of-the-art community detection algorithms, namely Louvain, Greedy Modularity, Label Propagation, and Infomap, and analyzed the intrinsic architecture and organization of the outcomes. In addition, to offer a more robust and consensual perspective on the results, we introduced a novel Rough Clustering method that allows a refined interpretation of communities within the overall network structure.