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GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games
[article]
2022
arXiv
pre-print
Explaining machine learning models is an important and increasingly popular area of research interest. The Shapley value from game theory has been proposed as a prime approach to compute feature importance towards model predictions on images, text, tabular data, and recently graph neural networks (GNNs) on graphs. In this work, we revisit the appropriateness of the Shapley value for GNN explanation, where the task is to identify the most important subgraph and constituent nodes for GNN
arXiv:2201.12380v5
fatcat:72j4duzkfbd3nme2rbtjeyxqvq