PlasmoData.jl -- A Julia Framework for Modeling and Analyzing Complex Data as Graphs

TL;DR

PlasmoData.jl is a Julia framework that models complex datasets as graphs, enabling advanced analysis using topology, graph theory, and machine learning, demonstrated through diverse real-world applications.

Contribution

It introduces a flexible DataGraph abstraction in Julia for representing various data types as graphs, facilitating analysis with graph-based tools and machine learning methods.

Findings

Successfully applied to image classification with topological features

Detected abnormal events in multivariate time series data

Enabled navigation of connectivity in technology pathways

Abstract

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.