Explainability of Graph Neural Networks

PhD Qualifying Examination


Title: "Explainability of Graph Neural Networks"

by

Miss Ge LV


Abstract:

In recent years, Graph Neural Networks (GNNs) have received significant 
attention from both industry and academic world owing to their outstanding 
performance in many graphbased tasks. However, black-box nature of these models 
hinders them from being trustworthy tools with transparent decision making 
mechanism. As a result, extensive efforts have been devoted to promoting 
explainability of GNNs and the area is experiencing rapid developments. In this 
survey, we aim to provide intuitive understanding and inspiring insights of 
various techniques by introducing the problem setting of explaining a GNN and 
reviewing the state-of-the-art (SOTA) GNN explainability methods. We also 
revisit the evaluation acting for the task, including the commonly used 
datasets and quantitative metrics.


Date:			Monday, 29 May 2023

Time:                  	4:00pm - 6:00pm

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:	Prof. Lei Chen (Supervisor)
 			Prof. Nevin Zhang (Chairperson)
 			Dr. Minhao Cheng
 			Dr. Dan Xu


**** ALL are Welcome ****