$k$ -Context Technique: A Method for Identifying Dense Subgraphs in a Heterogeneous Information Network

Most real-life problems can be modeled using heterogeneous networks, as they consist of several interconnected entities. However, researchers often study most of these problems as projected homogeneous networks. In such networks, different entities can be merged, some entities can be removed, or interactions among some entities can be overlooked, which results in a significant information loss. Moreover, these networks often overlook different structural possibilities in heterogeneous networks. Different structural possibilities may include a hierarchical network topology or a centralized network topology. Mining on different structural topologies of a heterogeneous information network may lead to a better understanding of the behavior and characteristics of the individual entities and the network as a whole. This article proposes a novel approach named $k$ -context technique for extracting dense subgraphs from different structural topologies of the heterogeneous information network. The approach has been evaluated using both synthetic and real-life data sets, and it compared the results with an existing algorithm for finding dense subgraphs from a homogeneous information network.