Scalable OLAP and mining of information networks

  • Authors:
  • Jiawei Han;Xifeng Yan;Philip S. Yu

  • Affiliations:
  • Univ. of Illinois at Urbana-Champaign;Univ. of California at Santa Barbara;University of Illinois at Chicago

  • Venue:
  • Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
  • Year:
  • 2009

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Abstract

With the ubiquity of information networks and their broad applications, there have been numerous studies on the construction, online analytical processing, and mining of information networks in multiple disciplines, including social network analysis, World-Wide Web, database systems, data mining, machine learning, and networked communication and information systems. In this tutorial, we present an organized picture on scalable OLAP (online analytical processing) and mining of information networks, with the inclusion of the following topics: (1) an introduction to information networks and information network analysis, (2) general statistical behavior of information networks, (3) mining frequent subgraphs in large graphs and networks, (4) data integration, data cleaning and data validation in information networks, (5) clustering graphs and information networks, (6) classification of graphs and information networks; (7) summarization and simplification of graphs and information networks, (8) OLAP and multidimensional analysis of information networks, (9) evolution of dynamic information networks, and (10) research challenges on OLAP and mining of information networks.