Adaptive Structural Network Detection and Anomaly Analysis Engine

Adaptive Structural Network Detection and Anomaly Analysis Engine This repository contains an experimental computational algorithm designed to detect structural networks embedded in noisy spatial point clouds. The pipeline implements a multi-stage analysis framework including: • filament and branch detection • branch purification and filtering • network graph extraction • anomaly and structural irregularity detection • adaptive parameter optimization The system was tested on synthetic datasets containing noisy filament-like and vessel-like structures, demonstrating the ability to recover hidden structural networks and identify abnormal regions within complex point distributions. Potential application domains include: • cosmology (cosmic web filament detection) • medical imaging (vascular network analysis) • industrial inspection (pipeline and fiber network monitoring) • materials science (structural defect detection) • spatial graph analysis in complex systems The algorithm focuses on robust structure extraction under high noise conditions and includes adaptive parameter tuning to automatically select optimal detection configurations. This project represents a prototype structural network analysis engine for research and experimental applications. Figures included in this release: • V16 structural network detection pipeline • V17 benchmark comparison between baseline and proprietary algorithm • V17 filament extraction comparison • V18 anomaly detection visualization