PlasmoData.jl -- A Julia Framework for Modeling and Analyzing Complex
  Data as Graphs
David L Cole Victor M Zavala
Datasets encountered in scientific and engineering applications appear in
complex formats (e.g., images, multivariate time series, molecules, video, text
strings, networks). Graph theory provides a unifying framework to model such
datasets and enables the use of powerful tools that can help analyze,
visualize, and extract value from data. In this work, we present PlasmoData.jl,
an open-source, Julia framework that uses concepts of graph theory to
facilitate the modeling and analysis of complex datasets. The core of our
framework is a general data modeling abstraction, which we call a DataGraph. We
show how the abstraction and software implementation can be used to represent
diverse data objects as graphs and to enable the use of tools from topology,
graph theory, and machine learning (e.g., graph neural networks) to conduct a
variety of tasks. We illustrate the versatility of the framework by using real
datasets: i) an image classification problem using topological data analysis to
extract features from the graph model to train machine learning models; ii) a
disease outbreak problem where we model multivariate time series as graphs to
detect abnormal events; and iii) a technology pathway analysis problem where we
highlight how we can use graphs to navigate connectivity. Our discussion also
highlights how PlasmoData.jl leverages native Julia capabilities to enable
compact syntax, scalable computations, and interfaces with diverse packages.