Viewing the World Through a Network Lens

Doctoral Dissertation

Abstract

Most conventional data structures and data analysis methods were designed with simple transaction data in mind. However, data miners are increasingly presented with more complex datasets that have embedded within them some relationships or dependencies. Incorporating these relationships into the data mining process can pose both algorithmic as well as computational challenges, but there is also a tremendous opportunity to leverage them as an additional source of information. Indeed, we believe that there is relational structure in every dataset, which can be exploited for analysis and learning if a suitable data representation is used. In this dissertation, we take a look at the world through a “network lens”, that is, we advocate the use of networks for representing and analyzing complex datasets from various domains. First, we propose a methodological advance in the form of a novel algorithm for identifying community structure in networks that is relevant across many domains. Second, we present applications wherein we impose the network view on datasets that do not contain explicit relationships and show how the “network lens” brings into focus some interesting and potentially useful patterns in the data. Specifically, in climate science we demonstrate the value of networks as a unified framework for descriptive analysis and predictive modeling, which has led to some novel insights in the domain.

Attributes

Attribute NameValues
URN
  • etd-04152011-122049

Author Karsten Steinhaeuser
Advisor Jessica Hellman
Contributor Nitesh Chawla, Committee Member
Contributor Patrick Flynn, Committee Member
Contributor Jessica Hellman, Committee Chair
Contributor Auroop Ganguly, Committee Member
Contributor Edward Bensman, Committee Member
Contributor Kevin Bowyer, Committee Member
Degree Level Doctoral Dissertation
Degree Discipline Computer Science and Engineering
Degree Name PhD
Defense Date
  • 2010-12-07

Submission Date 2011-04-15
Country
  • United States of America

Subject
  • data mining

  • community structure

  • social networks

  • complex networks

  • climate data

Publisher
  • University of Notre Dame

Language
  • English

Record Visibility and Access Public
Content License
  • All rights reserved

Departments and Units

Files

Please Note: You may encounter a delay before a download begins. Large or infrequently accessed files can take several minutes to retrieve from our archival storage system.