  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.
