Graph Unlearning with Efficient Partial Retraining

arXiv:2403.07353v1 Announce Type: new
Abstract: 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 …

abstract applications arxiv cs.cr cs.lg data gnns graph graph data graph neural networks however networks neural networks performance reliability retraining solution success training type unlearning world