Psychiatry
Standard network analysis conflates structurally interesting connectivity with the trivial consequences of having many edges. Edge-level discrete curvature separates genuine structural anomalies from degree artifacts.
Curvature in Psychiatric Mechanism Networks
Degree-Aware Edge Curvature Decomposes Transdiagnostic Bridge Structure Across Disease Networks
Zenodo 10.5281/zenodo.21251608
A 130-node, 218-edge knowledge graph from an expert-adjudicated catalog of 84 psychiatric mechanistic claims linked through 19 shared biological mechanisms. Ollivier-Ricci curvature applied to edges, combined with a degree-preserving permutation null, shows that node-level curvature remains a degree artifact (r = -0.71, p < 0.001), while edge-level curvature resolves genuine structure. Four edges carry more negative curvature than degree predicts, all involving depression or bipolar disorder bridging cross-domain mechanisms: the depression-convergence hub (z = -5.46), depression-metabolic (z = -3.93), bipolar-metabolic (z = -3.33), and depression-circadian (z = -2.62). Disconfirmed claims occupy significantly lower-curvature positions than all other verdict tiers (Cohen’s d = 0.89, n = 84). On seven independent graphs spanning five data types and three disease domains, within-cluster edges have higher curvature than cross-cluster edges in all seven (4/6 significant, d = 0.46-1.55).