Using graph machine learning for imbalanced data

In this section, we will see when graphs can be useful tools in machine learning, when to use graph ML models in general, and how they can be helpful on certain kinds of imbalanced datasets. We’ll also be exploring how graph ML models can outperform classical models such as XGBoost on certain imbalanced datasets.

Graphs are incredibly versatile data structures that can represent complex relationships and structures, from social networks and web pages (think of links as edges) to molecules in chemistry (consider atoms as nodes and the bonds between them as edges) and various other domains. Graph models allow us to represent the relationships in data, which can be helpful to make predictions and gain insights, even for problems where the relationships are not explicitly defined.

Understanding graphs

Graphs are the foundation of graph ML, so it’s important to understand them first. In the context of computer science...