We propose a general theoretical framework for detecting and characterizing latent entities in complex systems through the analysis of structural perturbations in observable data. The central thesis is that the deliberate suppression of information in a densely connected system generates measurable structural anomalies in adjacent nodes—an effect that admits precise mathematical characterization through divergence measures. We formalize this intuition through the concept of Informational Tension (τ), a local measure of entropic deviation based on the Jensen-Shannon Divergence between observed and expected neighborhood distributions. We derive the Concealment Cost (Ω) as a global measure scaling superlinearly with connectivity: Ω ~ d^ (1+α). Key Results (v3. 0): 1. Temporal Dynamics: The tension diffusion equation ∂τ/∂t = -αLτ + S (v, t) governs how suppression signatures propagate through networks. The Velocity of Silence vₛ = α√λ₁·ℓ̄ enables estimation of suppression age. 2. The Yharim Limi (Υ): We prove that the detectability threshold Υ = √ (2 log (1/α) ) remains valid for JSD-based tension in scale-free networks, despite infinite degree variance, because JSD boundedness guarantees finite tension variance. 3. The Sniper Effect: Low-degree nodes can exhibit high tension when their neighbors are well-connected—validated empirically with the "Marak" incident (faker. js, January 2022) where d=2 but τ=0. 31 exceeded detection thresholds. 4. Cross-Domain Validation: Empirical confirmation across software ecosystems (GitHub) and ecological networks (Burkle et al. , 2013) demonstrates domain equivalence: identical mathematics governs species extinction and developer banning. The framework provides both static detection (where is suppression? ) and dynamic forensics (when did suppression occur? ). Version History: - v1. 0 (Dec 2025): Initial static framework- v3. 0 (Jan 2026): Added temporal dynamics (Velocity of Silence), JSD-Yharim proof, Sniper Effect, empirical validation
Matheus Grego (Sat,) studied this question.