Representing Big Data as Networks: New Methods and Insights

Doctoral Dissertation

Abstract

Our world produces massive data every day; they exist in diverse forms, from pairwise data and matrix to time series and trajectories. Meanwhile, we have access to the versatile toolkit of network analysis. Networks also have different forms; from simple networks to higher-order network, each representation has different capabilities in carrying information. For researchers who want to leverage the power of the network toolkit, and apply it beyond networks data to sequential data, diffusion data, and many more, the question is: how to represent big data and networks? This dissertation makes a first step to answering the question. It proposes the higher-order network, which is a critical piece for representing higher-order interaction data; it introduces a scalable algorithm for building the network, and visualization tools for interactive exploration. Finally, it presents broad applications of the higher-order network in the real-world.

Attributes

Attribute NameValues
Author Jian Xu
Contributor Tijana Milenkovec, Committee Member
Contributor Nitesh Chawla, Research Director
Contributor Zoltan Toroczkai, Committee Member
Contributor David Lodge, Committee Member
Degree Level Doctoral Dissertation
Degree Discipline Computer Science and Engineering
Degree Name PhD
Defense Date
  • 2017-05-19

Submission Date 2017-07-17
Subject
  • higher-order network

  • network science

  • data mining

Record Visibility Public
Content License
  • All rights reserved

Departments and Units

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