Evidence for a Functional Proximity Law in Multilayer Networks
This law provides a falsifiable, testable principle for understanding hub persistence across layers in multilayer networks, with implications for network science across multiple domains.
The paper proposes and validates the Functional Proximity Law, which states that hub importance scores in multilayer networks persist more strongly between functionally similar layers than dissimilar ones. Across 17 pre-registered experiments, 14 confirmed the law (p ~ 0.006), with 8 canonical domains reaching p < 0.05 individually and an external validation on the C. elegans connectome yielding r = 0.777 (p = 0.004).
Hub importance scores in multilayer networks persist more strongly between functionally similar layers than dissimilar ones. We call this the Functional Proximity Law and test it across 17 pre-registered experiments: 12 canonical domains (9 confirmed, 3 denied; molecular biology, neuroscience, computer systems, ecology, linguistics) plus 5 external validations on independently-authored datasets. Eight canonical domains reach p < 0.05 individually; the directional inequality holds in all 9 confirmed. Three DENIED domains reveal named structural boundary conditions that narrow the law's scope. A fully external validation on the C. elegans connectome -- where both data and layer definitions are independent of the authors -- yields r = 0.777 (p = 0.004). Binomial probability of 14/17 pre-registered confirmations by chance: p ~ 0.006. The law is falsifiable, makes testable directional predictions, and identifies the structural conditions under which it fails.