Explainability Techniques for Graph Convolutional Networks

Add code
May 31, 2019
Federico Baldassarre, Hossein Azizpour
Figure 1 for Explainability Techniques for Graph Convolutional Networks
Figure 2 for Explainability Techniques for Graph Convolutional Networks
Figure 3 for Explainability Techniques for Graph Convolutional Networks
Figure 4 for Explainability Techniques for Graph Convolutional Networks

Share this with someone who'll enjoy it:

Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon

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.

* Accepted at the ICML 2019 Workshop "Learning and Reasoning with Graph-Structured Representations" (poster + spotlight talk)  
View paper onarxiv icon

Share this with someone who'll enjoy it:

Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon