Training-Free Archetype Typing of Conspiracy Networks from Call-Detail Records (Sanjaal)

Abstract

We study an on-device, training-free classifier that types a multi-suspect call-detail-record (CDR) group by the clandestine-network archetype it most resembles — one of nineteen documented topologies — or as benign. Each archetype is a synthesised neuron: the measured feature-space centroid of canonical exemplars, scored by a uniform-weight Gaussian kernel in standardised space. On a replicated synthetic benchmark (8 runs, seeds disjoint from calibration), 20-class typing reaches 86.3% top-1 accuracy (95% CI 85.8–86.8%), macro-F1 0.856; an open-set abstention gate lifts selective accuracy to 88.5% at 95.0% coverage. The central result is negative and we lead with it: the risk score that separates conspiracies from a trivial benign null at AUC 1.00 separates them from realistic benign-but-busy groups (families, call-centres, rallies, fleets, carpools) at AUC 0.25 — below chance — flagging 83% of lawful cohesive groups. Structure does not encode intent. Accuracy also falls from 83.8% to 33.3% under realistic partial observation, and 20-member mixed conspiracies defeat single-label typing (30% confidence) while still being correctly decomposed. We conclude the engine’s defensible role is a structure-typer and lead-generator for a human analyst, not an autonomous detector. All numbers come from the shipping engine; the benchmark is synthetic (separability + robustness, not field validation). 1.2em