The core-periphery structure is a mesoscale topological structure that refers to the presence of a dense core and a sparse periphery. The core-periphery structure has been discovered in financial, biological, and technological networks. Various methods for detecting core-periphery structure have been proposed, exploring different discrete or continuous models and single or multiple core-periphery groups. A method that implements multiple-group detection and edge-direction dependency to core-periphery detection is yet to be researched. This report proposes an algorithm to extract the core-periphery structure that satisfies multiple-group and edge-direction dependency requirements. This algorithm features a heuristic process and can process a large-scale network in an acceptable amount of time. Details of the theory behind the proposed algorithm are presented. The algorithm is tested on synthetic, random scale-free, and sampled dark web networks to verify the basic and advanced feasibility. Finally, in-depth analysis with the knowledge of core-periphery structure on a large-scale dark web network sample is presented.