Heterogeneous information networks (HINs) represent different types of entities and relationships between them. Exploring and mining HINs relies on metapath queries that identify pairs of entities connected by relationships of diverse semantics. While the real-time evaluation of metapath query workloads on large, web-scale HINs is highly demanding in computational cost, current approaches do not exploit interrelationships among the queries. In this paper, we present Atrapos, a new approach for the real-time evaluation of metapath query workloads that leverages a combination of efficient sparse matrix multiplication and intermediate result caching. Atrapos selects intermediate results to cache and reuse by detecting frequent sub-metapaths among workload queries in real time, using a tailor-made data structure, the Overlap Tree, and an associated caching policy. Our experimental study on real data shows that Atrapos accelerates exploratory data analysis and mining on HINs, outperforming off-the-shelf caching approaches and state-of-the-art research prototypes in all examined scenarios.

We present SPHINX, a system for metapath-based entity exploration in Heterogeneous Information Networks (HINs). SPHINX allows users to define different views over a HIN based on both automatically selected and user-defined meta-paths. Then, entity ranking and similarity search can be performed over these views to find and explore entities of interest, taking also into account any spatial or temporal properties of entities. A Web-based user interface is provided to facilitate users in performing the various functionalities supported by the system, including metapath-based view definition, index construction, search parameters specification, and visual comparison of the results.

Due to the rapidly increasing number of scientific articles, finding valuable work for further research has become tedious and time consuming. To alleviate this issue, search engines have used citation-based article impact ranking. However, most engines rely on very simplistic impact measures (usually the citation count) and make the problematic assumption that there is a one-size-fits-all impact measure. To address these problems, we present BIP! Finder, a search engine that facilitates the identification of valuable articles by exploiting two different impact measures, each capturing a different aspect of the article impact. In addition, BIP! Finder provides many useful features (article comparison, intuitive visualisations, article bookmarking mechanism, etc.) making it a powerful addition to the researcher's toolbox.