In this paper, we study graph representation learning, and we show that data augmentation that preserves semantics can be learned and used to produce interpretations. Our framework, which we named INGENIOUS, creates inherently interpretable embeddings and eliminates the need for costly additional post-hoc analysis. We also introduce additional metrics addressing the lack of formalism and metrics in the understudied area of unsupervised-representation learning interpretability. Our results are supported by an experimental study applied to both graph-level and node-level tasks and show that interpretable embeddings provide state-of-the-art performance on subsequent downstream tasks.
Gregory Scafarto, Madalina Ciortan, Simon Tihon, Quentin Ferre, Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings. In Proc. of The 15th Asian Conference on Machine Learning (ACML 2023), November 2023.