# Sampling a Network to Find Nodes of Interest

## TL;DR

This paper introduces REDLEARN, an algorithm designed to efficiently sample dark social networks and identify nodes of interest despite individuals concealing information, achieving significant improvements over existing strategies.

## Contribution

The paper presents a novel sampling algorithm, REDLEARN, tailored for dark networks, accounting for lying scenarios, and demonstrating superior performance on real-world data.

## Key findings

- REDLEARN outperforms existing strategies by up to 340%.
- The algorithm effectively handles scenarios with individuals concealing information.
- Results are validated on multiple real-world multilayered networks.

## Abstract

The focus of the current research is to identify people of interest in social networks. We are especially interested in studying dark networks, which represent illegal or covert activity. In such networks, people are unlikely to disclose accurate information when queried. We present REDLEARN, an algorithm for sampling dark networks with the goal of identifying as many nodes of interest as possible. We consider two realistic lying scenarios, which describe how individuals in a dark network may attempt to conceal their connections. We test and present our results on several real-world multilayered networks, and show that REDLEARN achieves up to a 340% improvement over the next best strategy.