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Sunday, August 11 • 9:10am - 10:00am
SNAKDD: Keynote Speech 1: From Social Networks to Heterogeneous Social and Information Networks: A Data Mining Perspective

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ABSTRACT: Many people treat social networks as homogeneous networks, modeled mainly as people network. Actually, people and informational objects are interconnected, forming gigantic, interconnected, integrated social and information networks. By structuring these data objects into multiple types, such networks become semi-structured heterogeneous social and information networks. Most real world applications that handle big data, including interconnected social media and social networks, medical information systems, online e-commerce systems, or database systems, can be structured into typed, heterogeneous social and information networks. For example, in a medical care network, objects of multiple types, such as patients, doctors, diseases, medication, and links such as visits, diagnosis, and treatments are intertwined together, providing rich information and forming heterogeneous information networks. Effective analysis of large-scale heterogeneous social and information networks poses an interesting but critical challenge. In this talk, we present a set of data mining scenarios in heterogeneous social and information networks and show that mining typed, heterogeneous networks is a new and promising research frontier in data mining research. Departing from many existing network models that view data as homogeneous graphs or networks, the semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and can uncover surprisingly rich knowledge from interconnected data. This heterogeneous network modeling will lead to the discovery of a set of new principles and methodologies for mining interconnected data. The examples to be used in this discussion include (1) meta path-based similarity search, (2) rank-based clustering, (3) rank-based classification, (4) meta path-based link/relationship prediction, (5) relation strength-aware mining, as well as a few other recent developments. We will also point out some promising research directions and provide convincing arguments on that mining heterogeneous information networks could be a key to social intelligence mining.


Jiawei Han

Jiawei Han, Abel Bliss Professor of Computer Science, University of Illinois at Urbana-Champaign. He has been researching into data mining, information network analysis, database systems, and data warehousing, with over 600 journal and conference publications. He has chaired or served on many program committees of international conferences, including PC co-chair for KDD, SDM, and ICDM conferences, and Americas Coordinator for VLDB conferences. He... Read More →

Sunday August 11, 2013 9:10am - 10:00am
Chicago 8

Attendees (5)