DarkNetExplorer (DNE): Exploring dark multi-layer networks beyond the resolution limit

Abstract Timely identification of terrorist networks within civilian populations could assist security and intelligence personnel to disrupt and dismantle potential terrorist activities. Finding “small” and “good” communities in multi-layer terrorist networks, where each layer represents a particular type of relationship between network actors, is a vital step in such disruption efforts. We propose a community detection algorithm that draws on the principles of discrete-time random walks to find such “small” and “good” communities in a multi-layer terrorist network. Our algorithm uses several parallel walkers that take short independent random walks towards hubs on a multi-layer network to capture its structure. We first evaluate the correlation between nodes using the extracted walks. Then, we apply an agglomerative clustering procedure to maximize the asymptotical Surprise, which allows us to go beyond the resolution limit and find small and less sparse communities in multi-layer networks. This process affords us a focused investigation on the more important seeds over random actors within the network. We tested our algorithm on three real-world multi-layer dark networks and compared the results against those found by applying two existing approaches – Louvain and InfoMap – to the same networks. The comparative analysis shows that our algorithm outperforms the existing approaches in differentiating “small” and “good” communities.