2026-09-19T16:29:30Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/72873 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Chang, Kevin C. Chang, Kevin C. Han, Jiawei Zhai, ChengXiang Krishnaswamy, Shonali Fang, Yuan 2015-01-21T19:49:04Z 2015-01-21T19:49:04Z 2014-12 2015-01-21 2014-12 "Graphs are powerful vehicles to represent data objects that are interconnecting or interacting with each other. We explore random walks on various kinds of graph to address different searching and mining scenarios. This dissertation focuses on two symmetric forms of random walk called the forward walk and backward walk, which can be applied to enrich three key tasks in searching and mining, namely, extraction, ranking and classification, in novel ways. More specifically, we enhance extraction with the metrics of probabilistic precision and recall, ranking with the senses of importance and specificity, and classification with heterogeneous contexts in terms of relationship type and confidence level. We further study the underpinning principle of random walks on a graph, which is often known as the smoothness assumption. We argue that smoothness is a pointwise property and requires probabilistic modeling. Thus, we propose a new framework to define pointwise smoothness probabilistically on a graph, which unifies two different ""modes"" of smoothness corresponding to the forward and backward random walks, respectively. Finally, our graph-based random walk solutions have consistently demonstrated promising empirical results for a wide range of searching and mining applications." Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2014-09-03T16:12:56Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Fang_Yuan.pdf: 3041597 bytes, checksum: 15ccf154ae022d2c9f5b3ecce9239361 (MD5) Made available in DSpace on 2015-01-21T19:49:04Z (GMT). No. of bitstreams: 1 Yuan_Fang.pdf: 3041597 bytes, checksum: 15ccf154ae022d2c9f5b3ecce9239361 (MD5) http://hdl.handle.net/2142/72873 en Copyright 2014 Yuan Fang Graph searching and mining random walk semi-supervised learning smoothness Walking forward and backward: towards graph-based searching and mining text Computer Science 1434 Computer Science 0112 University of Illinois at Urbana-Champaign Dissertation Ph.D. PHD:Computer Science -UIUC 10KS0112PHD