Author ORCID Identifier
https://orcid.org/0000-0002-9303-7835
Date of Award
Summer 8-11-2020
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science
First Advisor
Rafal Angryk
Abstract
Graphs and time series are two of the most ubiquitous representations of data of modern time. Representation learning of real-world graphs and time-series data is a key component for the downstream supervised and unsupervised machine learning tasks such as classification, clustering, and visualization. Because of the inherent high dimensionality, representation learning, i.e., low dimensional vector-based embedding of graphs and time-series data is very challenging. Learning interpretable features incorporates transparency of the feature roles, and facilitates downstream analytics tasks in addition to maximizing the performance of the downstream machine learning models. In this thesis, we leveraged tensor (multidimensional array) decomposition for generating interpretable and low dimensional feature space of graphs and time-series data found from three domains: social networks, neuroscience, and heliophysics. We present the theoretical models and empirical results on node embedding of social networks, biomarker embedding on fMRI-based brain networks, and prediction and visualization of multivariate time-series-based flaring and non-flaring solar events.
DOI
https://doi.org/10.57709/18617943
Recommended Citation
Hamdi, Shah Muhammad, "Learning Interpretable Features of Graphs and Time Series Data." Dissertation, Georgia State University, 2020.
doi: https://doi.org/10.57709/18617943
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