Discovering roles and types from hierarchical information networks

Graphs are all around us. They can be made to model countless real-world phenomena ranging from the social to the scientific including information, media, biology, chemistry, medical systems, and e-commerce systems. These graphs are called information networks because they represent bits of information and their relationships. In my thesis, I investigate the principles and methodologies for discovering roles and types in large information networks by exploring hierarchies inherent within the networks. I focus on the Web-information network, as well as specialized sub-networks like Wikipedia, where I aim to determine the type of a Web page or Wiki page as well as its position in the type-hierarchy (e.g., professor, student, and course exist within a department within a college) and their relationships to each other. By identifying multiple types and roles such networks become {\em heterogeneous} information networks. This new information can then be used to answer expressive queries on the network and allows for the exploration of additional properties about the network that were previously unknown.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Weninger, Timothy

Contributors dc:contributor

• Han, Jiawei
• Liu, Bing
• Sinha, Saurabh
• Chang, Kevin C-C.

• Web
• Hierarchy
• Link Paths
• Entity Discovery

Statement dc:rights

• Copyright 2013 by Timothy Edwards Weninger. All rights reserved.

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/45402

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/45402