提出新框架提升时空预测公平性，兼顾准确与一致性
HiMoE: Heterogeneity-Informed Mixture-of-Experts for Fair Spatial-Temporal Forecasting
- 基于节点异质性设计图卷积与专家混合模块
- 在4个真实数据集上性能优于基线至少9.22%
- 适合关注公平性与时序预测的算法研究者
在公平的时空预测任务中，确保各空间节点预测结果既准确又一致至关重要。然而现有训练方法对异质节点采用统一平均策略，导致预测目标固有偏差。由于时空预测具有多目标特性，平衡精度与一致性尤为困难。为此，本文提出异质性感知的专家混合框架（HiMoE），实现均匀且精确的时空预测。从模型架构看，设计异质性感知图卷积网络（HiGCN）应对趋势异质性，引入节点级专家混合（NMoE）模块处理节点基数异质性。从评估角度看，提出STFairBench基准，从训练到评估阶段均考虑公平性。在四个真实数据集上的实验表明，HiMoE在所有评估指标上均达到领先水平，最优基线提升至少9.22%。
原文摘要 · Abstract (English)
Achieving both accurate and consistent predictive performance across spatial nodes is crucial for ensuring the validity and reliability of outcomes in fair spatial-temporal forecasting tasks. However, existing training methods treat heterogeneous nodes with a fully averaged perspective, resulting in inherently biased prediction targets. Balancing accuracy and consistency is particularly challenging due to the multi-objective nature of spatial-temporal forecasting. To address this issue, we propose a novel Heterogeneity-Informed Mixture-of-Experts (HiMoE) framework that delivers both uniform and precise spatial-temporal predictions. From a model architecture perspective, we design the Heterogeneity-Informed Graph Convolutional Network (HiGCN) to address trend heterogeneity, and we introduce the Node-wise Mixture-of-Experts (NMoE) module to handle cardinality heterogeneity across nodes. From an evaluation perspective, we propose STFairBench, a benchmark that handles fairness in spatial-temporal prediction from both training and evaluation stages. Extensive experiments on four real-world datasets demonstrate that HiMoE achieves state-of-the-art performance, outperforming the best baseline by at least 9.22% across all evaluation metrics.
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