Year: 2010
Type: dissertation
Abstract: The first part of this thesis explores issues surrounding the manipulation of PageRank, a popular link-analysis based reputation system for the web. Page-Rank is an essential part of web search, but it is also subject to manipulation by selfish web authors. We develop an alternative to PageRank, based on ex-pected hitting time in a random walk, that is provably robust to manipulation by outlinks. We then study the effects of manipulation on the network itself by analyzing the stable outcomes of the PageRank Game, a network-formation model where web pages place outlinks strategically to maximize PageRank. The second part of the thesis explores probabilistic inference algorithms for a family of models called collective hidden Markov models. These generalize hidden Markov models (HMMs) to the situation in which one views partial in-formation about many indistinguishable objects that all behave according to the same Markovian dynamics. Collective HMMs are motivated by an important problem in ecology: inferring bird migration paths from a large database of ob-servations.
Source: eCommons (Cornell University)
Author: Daniel Sheldon
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2135202194