2020/02/19 by Manuel S. Mariani, Manuel Sebastian Mariani, María J. Palazzi +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Neuroscience · Physics and Astronomy · Psychology · #Artificial intelligence #Block (permutation group theory) #Combinatorics #Complex Network Analysis Techniques #Computer science #Function (biology) #Functional Brain Connectivity Studies #Limit (mathematics) #Mathematics #Mental Health Research Topics #Modularity (biology) #Nestedness #Partition (number theory) #Property (philosophy) #Resolution (logic) #Theoretical computer science #cs.SI #physics.data-an #physics.soc-ph #q-bio.QM
paper · pdf · doi:10.1016/j.cnsns.2020.105545
published as Communications in Nonlinear Science and Numerical Simulation 94 (2021) 105545 · 12 pages, 4 figures, 1 table
arxiv created 2020/02/19 · openalex publication_date 2020/09/28 · arxiv updated 2020/10/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Originally a speculative pattern in ecological networks, the hybrid or compound nested-modular pattern has been confirmed, during the last decade, as a relevant structural arrangement that emerges in a variety of contexts --in ecological mutualistic system and beyond. This implies shifting the focus from the measurement of nestedness as a global property (macro level), to the detection of blocks (meso level) that internally exhibit a high degree of nestedness. Unfortunately, the availability and understanding of the methods to properly detect in-block nested partitions lie behind the empirical findings: while a precise quality function of in-block nestedness has been proposed, we lack an understanding of its possible inherent constraints. Specifically, while it is well known that Newman-Girvan's modularity, and related quality functions, notoriously suffer from a resolution limit that impairs their ability to detect small blocks, the potential existence of resolution limits for in-block nestedness is unexplored. Here, we provide empirical, numerical and analytical evidence that the in-block nestedness function lacks a resolution limit, and thus our capacity to detect correct partitions in networks via its maximization depends solely on the accuracy of the optimization algorithms.