Mining knowledge from interconnected data: a heterogeneous information network analysis approach

Authors:
Yizhou Sun;Jiawei Han;Xifeng Yan;Philip S. Yu
Affiliations:
University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL;University of California at Santa Barbara, Santa Barbara, CA;University of Illinois at Chicago, Chicago, IL
Venue:
Proceedings of the VLDB Endowment
Year:
2012

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Abstract

Most objects and data in the real world are interconnected, forming complex, heterogeneous but often semi-structured information networks. However, most people consider a database merely as a data repository that supports data storage and retrieval rather than one or a set of heterogeneous information networks that contain rich, inter-related, multi-typed data and information. Most network science researchers only study homogeneous networks, without distinguishing the different types of objects and links in the networks. In this tutorial, we view database and other interconnected data as heterogeneous information networks, and study how to leverage the rich semantic meaning of types of objects and links in the networks. We systematically introduce the technologies that can effectively and efficiently mine useful knowledge from such information networks.