Heterogenenous Information Networks
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About
Most real systems consist of multi-typed entities/objects with a variety of relationships between interacting and/or associated objects. These interactions and relationships between entities are naturally represented as information network graphs. Information networks are ubiquitous and well-established in the real world with examples such as publication networks, communication networks, the World Wide Web, or social networks. Nowadays, we have to handle a size of these information networks with ranges from hundreds up to millions and billions of nodes. By the rise of data integration, an increasing attention to information networks in academia and industry have been observed. Recently, new challenges have been introduced as we are not only concentrating on homogeneous data, but are rather faced to heterogeneous data derived from a variety of sources. A lot of information gets lost when scaling such information networks down to traditional homogeneous networks. Heterogeneous information networks (HINs) which got a lot of attraction in diverse application fields, provide a more general, natural, and rich representation of relationships between objects and semantic information than traditional networks. Consequently, the problem of understanding the vast amount of information modeled in HINs has received a lot of interest.