2026-09-20T18:01:43Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/42366 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Han, Jiawei Han, Jiawei Zhai, ChengXiang Roth, Dan Aggarwal, Charu C. Sun, Yizhou 2013-02-03T19:36:32Z 2013-02-03T19:36:32Z 2012-12 2013-02-03T19:36:32Z 2012-12 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. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2012-11-26T15:58:15Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Sun_Yizhou.pdf: 2648559 bytes, checksum: 131bbd8e442f161160beb11b44cd5420 (MD5) Made available in DSpace on 2013-02-03T19:36:32Z (GMT). No. of bitstreams: 2 Yizhou_Sun.pdf: 2648580 bytes, checksum: 40d1622bd7fe2ce79bd1f0d3ac23e401 (MD5) license.txt: 4057 bytes, checksum: 960182ae61554e18d74922afd09a37df (MD5) http://hdl.handle.net/2142/42366 en Copyright 2012 Yizhou Sun 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 Mining heterogeneous information networks text Computer Science 1434 Computer Science 0112 University of Illinois at Urbana-Champaign Dissertation Ph.D. PHD:Computer Science -UIUC 10KS0112PHD