Abstract
Knowledge is power but for interrelated data, knowledge is often hidden in massive links in heterogeneous information networks. We explore the power of links at mining heterogeneous information networks with several interesting tasks, including link-based object distinction, veracity analysis, multidimensional online analytical processing of heterogeneous information networks, and rank-based clustering. Some recent results of our research that explore the crucial information hidden in links will be introduced, including (1) Distinct for object distinction analysis, (2) TruthFinder for veracity analysis, (3) Infonet-OLAP for online analytical processing of information networks, and (4) RankClus for integrated ranking-based clustering. We also discuss some of our on-going studies in this direction.
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© 2009 Springer-Verlag Berlin Heidelberg
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Han, J. (2009). Mining Heterogeneous Information Networks by Exploring the Power of Links. In: Gavaldà , R., Lugosi, G., Zeugmann, T., Zilles, S. (eds) Algorithmic Learning Theory. ALT 2009. Lecture Notes in Computer Science(), vol 5809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04414-4_3
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DOI: https://doi.org/10.1007/978-3-642-04414-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04413-7
Online ISBN: 978-3-642-04414-4
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