Explainability Techniques for Graph Convolutional Networks

Positive and negative attributions for the solubility of sucrose


Graph Networks are used to make decisions in potentially complex scenarios but it is usually not obvious how or why they made them. In this work, we study the explainability of Graph Network decisions using two main classes of techniques, gradient-based and decomposition-based, on a toy dataset and a chemistry task. Our study sets the ground for future development as well as application to real-world problems.

International Conference on Machine Learning 2019 - Spotlight talk at the “Learning and Reasoning with Graph-Structured Representations” workshop
Federico Baldassarre
Federico Baldassarre
PhD Student in Deep Learning

My research focuses on explainability and reasoning in Deep Learning.

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