This tutorial was presented at the UMAP ‘23 conference, with Erasmo Purificato and Ernesto William De Luca. On the tutorial’s website, you can find the slides and the notebooks of the hands-on parts.

The proposed tutorial aimed to introduce the UMAP community to modern user profiling approaches leveraging graph neural networks (GNNs). We will begin by discussing the conceptual foundations of user profiling and GNNs and providing a literature review of the two topics.

We then present a systematic overview of the state-of-the-art GNN architectures designed for user profiling, including the types of data that are typically used for this purpose. We also discussed ethical considerations and beyond-accuracy perspectives (i.e. fairness and explainability), which can arise within the potential applications of adopting GNNs for user profiling.

In the practical session of the tutorial, attendees had the opportunity to understand concretely how recent GNN models for user profiling are built and trained with open-source tools and publicly available datasets. The audience was also engaged in investigating the impact of the presented models on case studies involving bias detection and mitigation, as well as user profiles explanations.

The tutorial ended with an analysis of existing and emerging open challenges in the field and their future research directions.