Abstract
ABSTRACTThe study of morphological modularity using anatomical networks is growing in recent years. A common strategy to find the best network partition uses community detection algorithms that optimize the modularity Q function. Because anatomical networks and their modules tend to be small, this strategy often produces two problems. One is that some algorithms find inexplicable different modules when one inputs 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 researchers 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 optimization of a global modularity function. NIMS may prove useful to identify modules also in small ecological and social networks.
Highlights
Anatomical network analysis has recently emerged as a new framework to study anatomy quantitatively using tools from network theory (Rasskin-Gutman and Esteve-Altava, 2014)
We can classify network-based modules as organizational modules: “Organizational morphological modules refer explicitly to the interactions postulated to be important in organismal construction or activity
node-based informed modularity strategy (NIMS) returns network modules that are robust to minor variations and avoids common pitfalls of other approaches by not requiring the optimization a global modularity function
Summary
Anatomical network analysis has recently emerged as a new framework to study anatomy quantitatively using tools from network theory (Rasskin-Gutman and Esteve-Altava, 2014). Anatomical studies using network analysis have focused on comparing the development, function, and evolution of morphological systems, from invertebrates to vertebrates, including extant and extinct organisms We can classify network-based modules as organizational modules: “Organizational morphological modules refer explicitly to the interactions postulated to be important in organismal construction or activity They invite observation or description in terms of mechanistic relations, whether variation among organisms is present or not. A more thoughtful discussion about the differences between anatomical network modules and shape co-variation modules in terms of concepts, methods, and limitation has been offered elsewhere (Esteve-Altava, 2017a)
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