Extracting Illicit-Rich Subgraph from Transaction Networks via Feed-Guided Shortest Paths

A research study introduces a motif-based coherent feed selection method for the Shortest Paths Network Search Algorithm (SPNSA) to optimize investigative subgraph extraction in Anti-Money Laundering (AML) transaction graphs. Testing on the IBM-AML benchmark and the Elliptic Bitcoin transaction graph showed that this topology-driven approach yields markedly higher illicit entity ratios than random or degree-based heuristics while keeping the extracted subgraphs compact.

This research addresses a fundamental operational challenge in financial crime compliance: isolating suspicious transaction pathways from massive networks without overwhelming investigative resources. Demonstrating that motif-based feed selection significantly enriches illicit entity concentrations enables compliance functions to accelerate alert triage across both conventional banking and cryptocurrency environments.

The efficacy of the Shortest Paths Network Search Algorithm (SPNSA) in AML network analysis is critically sensitive to the choice of the initial entity feed.

Forward consideration, not a verified fact.