Detecting covert communities in multi-layer networks: A network embedding approach

Abstract

Graph clustering is a fundamental task to discover community ties in multi-layer networks. In this paper, we propose a network embedding technique to find covert communities in multi-layer dark networks using a Log-BiLinear (LBL) approach. Recent works on graph clustering using network embedding have focused on new ways of learning representations of nodes and relations, upon which a classic clustering method is then used to identify the communities (clusters). However, these embedding approach does not yield good and accurate communities from the clustering task. Hence, we address this issue with a sequence-based network embedding technique on a multi-layer network. Our proposal learns structural representations of nodes and relations simultaneously by capturing the position of a given node within a set of neighboring anchor-set, and the type of connections between nodes in the anchor-set. To find the clusters (communities), clustering centroids are also learned as the representations of nodes and relations are extracted. Our solution is well-suited to detecting covert communities, such as terrorist networks. In our experiments on three real-world terrorist datasets and one synthetic network, our approach is found to deliver a higher level of accuracy in detecting covert communities compared with six baseline methods.