The authors introduce distribution-based, dynamic-weight, and cosine-similarity aggregation to make message-passing GNNs resilient to bit-flip errors, reporting accuracy improvements of 10 to 44 percent over existing aggregations.
A comprehensive survey on graph neural networks,
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Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors
The authors introduce distribution-based, dynamic-weight, and cosine-similarity aggregation to make message-passing GNNs resilient to bit-flip errors, reporting accuracy improvements of 10 to 44 percent over existing aggregations.