Query-Driven Discovery of Semantically Similar Substructures in Heterogeneous Networks
Heterogeneous information networks that contain multiple types of objects and links are ubiquitous in the real world, such as bibliographic networks, cyber-physical networks, and social media networks. Although researchers have studied various data mining tasks in information networks, interactive query-based network exploration techniques have not been addressed systematically, which, in fact, are highly desirable for exploring large-scale information networks. In this paper, the authors introduce and demonstrate their recent research project on query-driven discovery of semantically similar substructures in heterogeneous networks. Given a subgraph query, their system searches a given large information network and finds efficiently a list of subgraphs that are structurally identical and semantically similar.