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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.
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