  The graph data structure is a staple in mathematics, yet graph-based machine
learning is a relatively green field within the domain of data science. Recent
advances in graph-based ML and open source implementations of relevant
algorithms are allowing researchers to apply methods created in academia to
real-world datasets. The goal of this project was to utilize a popular graph
machine learning framework, GraphSAGE, to predict mergers and acquisitions
(M&A) of enterprise companies. The results were promising, as the model
predicted with 81.79% accuracy on a validation dataset. Given the abundance of
data sources and algorithmic decision making within financial data science,
graph-based machine learning offers a performant, yet non-traditional approach
to generating alpha.
