University of Notre Dame
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Learning from Complex Networks

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posted on 2021-12-07, 00:00 authored by Mandana Saebi

Real-world complex systems comprise several components that interact and influence each other via various mechanisms. In order to understand and model the underlying phenomena in such systems, it is important to solve two key challenges: (1) building network models that are an accurate representation of raw data, and (2) developing models that can learn from the network structure to capture the ongoing phenomena in the complex system.

In this dissertation, we propose novel methodologies to solve the above challenges. We first propose an efficient algorithm for higher-order networks to accurately represent the complex interactions in raw data. We then explore the applications of higher-order networks in various real-world problems such as modeling species spread through the global shipping network and anomaly detection in dynamic networks.

Towards learning from complex systems, we then move to build machine learning models that can learn from interacting components in the system. We explore the applications of these models in various real-world problems such as network embedding, relational reasoning, and predicting chemical reaction performance. Finally, we discuss the limitations and challenges of building machine learning methods for networks using real-world data and offer potential directions for future research.

History

Date Modified

2022-01-12

Defense Date

2021-08-31

CIP Code

  • 40.0501

Research Director(s)

Nitesh V. Chawla

Committee Members

Tim Weninger Meng Jiang Xiangliang Zhang

Degree

  • Doctor of Philosophy

Degree Level

  • Doctoral Dissertation

Language

  • English

Alternate Identifier

1291608004

Library Record

6157715

OCLC Number

1291608004

Program Name

  • Computer Science and Engineering

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