Learning from Complex Networks

Doctoral Dissertation


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.


Attribute NameValues
Author Mandana Saebi
Contributor Nitesh V. Chawla, Research Director
Contributor Tim Weninger, Committee Member
Contributor Meng Jiang, Committee Member
Contributor Xiangliang Zhang, Committee Member
Degree Level Doctoral Dissertation
Degree Discipline Computer Science and Engineering
Degree Name Doctor of Philosophy
Banner Code

Defense Date
  • 2021-08-31

Submission Date 2021-12-07
  • Machine learning

  • Graph mining

  • Deep learning

  • Higher-order networks

  • English

Record Visibility Public
Content License
  • All rights reserved

Departments and Units
Catalog Record

Digital Object Identifier


This DOI is the best way to cite this doctoral dissertation.


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.