Mining heterogeneous information networks

Real-world physical objects and abstract data entities are interconnected, forming gigantic networks. By structuring these objects and their interactions into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In my thesis, I investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including (1) ranking-based clustering, (2) meta-path-based similarity search and mining, (3) user-guided relation strength-aware mining, and many other potential developments. This thesis introduces this new research frontier and points out some promising research directions.

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

• Sun, Yizhou

Contributors dc:contributor

• Han, Jiawei
• Zhai, ChengXiang
• Roth, Dan
• Aggarwal, Charu C.

• information network
• social network
• heterogeneous information network
• data mining
• network schema
• meta-path
• clustering
• ranking
• similarity search
• relationship prediction
• user-guided meta-path selection
• relation strength-aware mining

Statement dc:rights

• Copyright 2012 Yizhou Sun

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/42366

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/42366