In today’s dynamic world of retail data science, uncovering patterns in transactional data is key to understanding customer behaviour. Graph Neural Networks (GNNs) are emerging as a game-changing approach—offering a fresh lens to model the intricate web of relationships hidden in this data. But while the potential is immense, capturing rich, meaningful information at scale remains a major hurdle.
In this talk, we’ll take you on a journey through how GNNs interpret transactional data differently from traditional methods—revealing a compelling interplay between local patterns and global structures within graph networks. We’ll explore how these multi-scale insights can enrich our models, helping us better reflect the underlying complexity of retail behaviour—and perhaps even reach beyond it.