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Heterogeneous Information Network Analysis and Applications pp 181–199Cite as

Schema-Rich Heterogeneous Network Mining

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Part of the book series: Data Analytics ((DAANA))

Abstract

Traditional heterogeneous information network usually has simple network schema, where there are a small number of types of nodes and links and meta paths are easily enumerated. However, in many real applications, some heterogeneous information networks have a huge number of types of nodes and links, and it is hard to depict their network schema. We call this kind of networks as schema-rich heterogeneous information network. For example, knowledge graph, constructed with \(<object, relation, object>\) tuples, can be considered as a schema-rich heterogeneous network, where there are usually tens of thousands of types of nodes and links. In this chapter, we introduce two data mining tasks on schema-rich heterogeneous network: link prediction and entity set expansion. Through these two tasks, we illustrate the challenges and potential solutions on mining this kind of more complex and popular heterogeneous networks.

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Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Chuan Shi

  2. University of Illinois at Chicago, Chicago, IL, USA

    Philip S. Yu

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  1. Chuan Shi
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  2. Philip S. Yu
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Correspondence to Chuan Shi .

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Shi, C., Yu, P.S. (2017). Schema-Rich Heterogeneous Network Mining. In: Heterogeneous Information Network Analysis and Applications. Data Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-56212-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-56212-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56211-7

  • Online ISBN: 978-3-319-56212-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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