Graphs, Words, and Communities : converging paths to interpretability with a frugal embedding framework

Room B011/B013

This research addresses the growing sustainability and interpretability concerns in representation learning by introducing a framework that efficiently embeds both graph data and text while requiring significantly fewer computational resources than current approaches. Human evaluation confirms that the resulting embeddings produce interpretable dimensions, allowing users to better understand how meaning emerges from the representation.