A Node-based Informed Modularity Strategy to Identify Organizational Modules in Anatomical Networks

Abstract

The use of anatomical networks to study the modular organization of morphological systems and their evolution is growing in recent years. A common strategy to find the best partition of anatomical networks into modules is to use a community detection algorithm that tries to optimize the modularity Q function. However, this strategy overlooks the fact that Q has a resolution limit for small modules, which is often the case in anatomical networks. This produces two problems. One is that some algorithms find inexplicable different modules when we input slightly different networks. The other is that algorithms find asymmetric modules in otherwise symmetric networks. These problems have discouraged researchers to use anatomical network analysis and boost criticisms to this methodology. Here, I propose a Node-based Informed Modularity Strategy (NIMS) to identify modules in anatomical networks that bypass resolution and sensitivity limitations by using a bottom-up approach. Starting with the local modularity around every individual node, NIMS returns the modular organization of the network by merging non-redundant modules and assessing their intersection statistically using combinatorial theory. Instead of acting as a black box, NIMS allows researcher to make informed decisions about whether to merge non-redundant modules. NIMS returns network modules that are robust to minor variation and does not require to optimize a global modularity function. NIMS may prove useful to identify modules also in small ecological and social networks.