Investigative Simulation: Towards Utilizing Graph Pattern Matching for Investigative Search

TL;DR

This paper introduces investigative simulation, an enhanced graph pattern matching approach tailored for law enforcement to efficiently identify persons of interest exhibiting suspicious behaviors, with improved relevance and prioritization capabilities.

Contribution

It extends existing dual simulation graph matching with categorical labels, pruning, and ranking schemes to better support investigative analysis for law enforcement.

Findings

Effective on large real-world datasets

Improves relevance of search results

Supports prioritization of investigative efforts

Abstract

This paper proposes the use of graph pattern matching for investigative graph search, which is the process of searching for and prioritizing persons of interest who may exhibit part or all of a pattern of suspicious behaviors or connections. While there are a variety of applications, our principal motivation is to aid law enforcement in the detection of homegrown violent extremists. We introduce investigative simulation, which consists of several necessary extensions to the existing dual simulation graph pattern matching scheme in order to make it appropriate for intelligence analysts and law enforcement officials. Specifically, we impose a categorical label structure on nodes consistent with the nature of indicators in investigations, as well as prune or complete search results to ensure sensibility and usefulness of partial matches to analysts. Lastly, we introduce a natural top-k ranking scheme that can help analysts prioritize investigative efforts. We demonstrate performance of investigative simulation on a real-world large dataset.