Topology as Logic: Structural Role Geometry Across Formal, Software, Biological, and Prebiotic Systems
It is unclear whether dependency graph topology can quantitatively recover what domain experts identify as the operational logic of a system, as opposed to merely correlating with it metaphorically. Prior work lacked a systematic cross-domain empirical test.
The authors apply the IRDME multilayer network framework and the Functional Proximity Law across seven substrates: two digital circuits, two formal proof corpora (Lean 4 mathlib4 and Coq Corelib), legacy COBOL software, cross-species neural connectomics, and a prebiotic autocatalytic network. All hypotheses were pre-registered before analysis. Hub persistence and rank divergence are measured using both degree-based and betweenness-based centrality across structural layers. The Lean 4 mathlib4 analysis confirms strong cross-layer hub correlation (r=0.777, p=0.004).
All seven pre-registered experiments show positive hub persistence. The primary methodological finding is that betweenness-based persistence (r=0.771 in the 4-bit ALU) dominates degree-based persistence in detecting load-bearing logic nodes. The ISCAS85 c432 benchmark confirmed the degree hypothesis (r=0.426, p=0.002, Spearman r=0.551). The Coq replication was directionally consistent but underpowered (r=0.288, n=17).
| Substrate | n | Primary metric | Finding |
|---|---|---|---|
| Digital (4-bit) | 29 | Betweenness r=0.771 (post-hoc); degree r=0.512, p=0.004 | CONFIRMED |
| Digital (c432) | 196 | degree r=0.426, p=0.002, Spearman r=0.551 | CONFIRMED |
| Formal (Lean) | 20 | Pearson r=0.777, Spearman r=0.733, p=0.004 | CONFIRMED |
| Formal (Coq) | 17 | degree r=0.288, p=0.287 | PARTIAL |
| Legacy SW (COBOL) | 14 | r=0.807, p=0.002 | CONFIRMED |
| Neural (cross-species) | 2952 | cosine>=0.90 | CONFIRMED |
| Prebiotic | 1 network | catalytic hub | CONFIRMED |
