Skip to content

Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings

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 EmbeddingsIn Proc. of The 15th Asian Conference on Machine Learning (ACML 2023), November 2023.

 

Click here to access the paper.

 

Releated Posts

Insights From Flink Forward 2024

In October, our CTO Sabri Skhiri attended the Flink Forward conference, held in Berlin, which marked the 10-year anniversary of Apache Flink. This event brought together experts and enthusiasts in the field of stream processing to discuss the latest advancements, challenges, and future trends. In this article, Sabri will delve into some of the keynotes and talks that took place during the conference, highlighting the noteworthy insights and innovations shared by Ververica and industry leaders.
Read More

Internships 2025

This document presents internships supervised by our consulting department or by our research & development department. Each project is an opportunity to feel both empowered and responsible for your own professional development and for your contribution to the company.
Read More