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.

及时查明平民人口中的恐怖主义网络可协助安全和情报人员破坏和拆除潜在的恐怖活动。在多层恐怖主义网络中找到“小”和“好”社区，其中每一层代表网络参与者之间的特定类型的关系，是此类破坏工作中至关重要的一步。我们提出了一种社区检测算法，该算法利用离散时间随机游走的原理来在多层恐怖网络中找到这样的“小”和“好”社区。我们的算法使用了几个并行步行器，它们对多层网络上的集线器进行了短暂的独立随机游走，以捕获其结构。我们首先使用提取的游程评估节点之间的相关性。然后，我们应用了一个聚类聚类程序来最大程度地增加无症状惊喜，这使我们能够超越分辨率极限，并在多层网络中找到较小且稀疏的社区。这个过程使我们能够集中研究网络中随机角色上更重要的种子。我们在三个现实世界的多层暗网络上测试了我们的算法，并将结果与​​通过将两种现有方法Louvain和InfoMap应用于同一网络发现的结果进行了比较。对比分析表明，在区分“小”社区和“好”社区方面，我们的算法优于现有方法。这个过程使我们能够集中研究网络中随机角色上更重要的种子。我们在三个现实世界的多层暗网络上测试了我们的算法，并将结果与​​通过将两种现有方法Louvain和InfoMap应用于同一网络发现的结果进行了比较。对比分析表明，在区分“小”社区和“好”社区方面，我们的算法优于现有方法。这个过程使我们可以集中研究网络中随机角色上更重要的种子。我们在三个现实世界的多层暗网络上测试了我们的算法，并将结果与​​通过将两种现有方法Louvain和InfoMap应用于同一网络发现的结果进行了比较。对比分析表明，在区分“小”社区和“好”社区方面，我们的算法优于现有方法。