  Graph Neural Networks (GNNs) have achieved remarkable success in various
real-world applications. However, GNNs may be trained on undesirable graph
data, which can degrade their performance and reliability. To enable trained
GNNs to efficiently unlearn unwanted data, a desirable solution is
retraining-based graph unlearning, which partitions the training graph into
subgraphs and trains sub-models on them, allowing fast unlearning through
partial retraining. However, the graph partition process causes information
loss in the training graph, resulting in the low model utility of sub-GNN
models. In this paper, we propose GraphRevoker, a novel graph unlearning
framework that better maintains the model utility of unlearnable GNNs.
Specifically, we preserve the graph property with graph property-aware sharding
and effectively aggregate the sub-GNN models for prediction with graph
contrastive sub-model aggregation. We conduct extensive experiments to
demonstrate the superiority of our proposed approach.
