Mining Heterogeneous Information Networks
published: Oct. 1, 2010, recorded: July 2010, views: 12001
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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. Moreover, with a great demand of research in this direction, there is a need of a systematic introduction of methods for analysis of information networks from multiple disciplines. Recently there have been some tutorials on structures and laws of homogeneous information networks and graphs. However, there are few systematic tutorials on mining a more important kind of networks, heterogeneous information networks, where information networks are formed by interconnected, multi-typed nodes and links. In this tutorial, we will present an organized picture on scalable mining of heterogeneous information networks, which complements existing tutorials on knowledge discovery in homogeneous information networks. The tutorial includes the following topics:
1. introduction: information networks and information network analysis, 2. data integration, data cleaning and data validation in heterogeneous information networks, 3. clustering and ranking in heterogeneous information networks 4. classification of heterogeneous information networks, 5. summarization, OLAP and multidimensional analysis in heterogeneous information networks, 6. evolution of dynamic heterogeneous information networks, and research challenges on mining heterogeneous information networks.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !