Achtung:

Sie haben Javascript deaktiviert!
Sie haben versucht eine Funktion zu nutzen, die nur mit Javascript möglich ist. Um sämtliche Funktionalitäten unserer Internetseite zu nutzen, aktivieren Sie bitte Javascript in Ihrem Browser.

Info-Icon Diese Seite ist nicht in Deutsch verfügbar
mikemacmarketing (https://commons.wikimedia.org/wiki/File:Artificial_Intelligence_&_AI_&_Machine_Learning_-_30212411048.jpg), "Artificial Intelligence & AI & Machine Learning", cropped, https://creativecommons.org/licenses/by/2.0/legalcode,
This image was originally posted to Flickr by mikemacmarketing at https://flickr.com/photos/152824664@N07/30212411048. 
Image via www.vpnsrus.com Bildinformationen anzeigen

mikemacmarketing (https://commons.wikimedia.org/wiki/File:Artificial_Intelligence_&_AI_&_Machine_Learning_-_30212411048.jpg), "Artificial Intelligence & AI & Machine Learning", cropped, https://creativecommons.org/licenses/by/2.0/legalcode, This image was originally posted to Flickr by mikemacmarketing at https://flickr.com/photos/152824664@N07/30212411048. Image via www.vpnsrus.com

Content

Graph Neural Networks (GNN) are used in many important applications in computer vision, natural language processing, and biology. For example, DeepMind's AlphaFold is a graph neural network that can accurately predict 3D models of protein structures and is accelerating research in nearly every field of biology. Understanding the predictions of graph neural networks is crucial for users of the models. However, conventional approaches for explainable artificial intelligence (XAI) are applicable to tables and images, but not to graph data because a node can have an arbitrary number of neighbors and the order of neighbors does not matter. Therefore, special XAI approaches for graphs are needed. The goal of this project group is to design novel algorithms for explaining the predictions of graph neural networks.

Related Work

Ying, Zhitao, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. "Gnnexplainer: Generating explanations for graph neural networks." Advances in neural information processing systems 32 (2019). https://proceedings.neurips.cc/paper/2019/file/d80b7040b773199015de6d3b4293c8ff-Paper.pdf

Contact

Dominik Köhler, Stefan Heindorf

 

Die Universität der Informationsgesellschaft