PlasmoData.jl — A Julia framework for modeling and analyzing complex data as graphs

Journal Article · 07 April 2024 · Computers and Chemical Engineering

DOI: https://doi.org/10.1016/j.compchemeng.2024.108679 · OSTI ID:2407242

Cole, David L. ^[1]; Zavala, Victor M. ^[2]

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1. Univ. of Wisconsin, Madison, WI (United States)
2. Univ. of Wisconsin, Madison, WI (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)

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. Further, our discussion also highlights how PlasmoData.jl leverages native Julia capabilities to enable compact syntax, scalable computations, and interfaces with diverse packages. Overall, we show that the DataGraph abstraction and PlasmoData.jl Julia package are able to model data within graphs and enable useful analysis.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Science Foundation (NSF)
• Grant/Contract Number: AC02-06CH11357
• OSTI ID: 2407242
• Journal Information: Computers and Chemical Engineering, Journal Name: Computers and Chemical Engineering Vol. 185; ISSN 0098-1354
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English