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IsleNet introduces a spatial-entropy-based partitioning method for spatiotemporal graph unlearning, enabling exact data removal with low computational cost while maintaining high accuracy for privacy regulations.
This paper proposes a spatiotemporal graph Transformer framework for traffic forecasting in edge computing, combining graph neural networks for spatial correlations and Transformer self-attention for long-range temporal dependencies. Experiments on real-world cellular data show it outperforms recurrent graph-based baselines like GCN-LSTM and GCN-GRU.