提出Coden模型，高效实现动态图的连续预测。
Coden: Efficient Temporal Graph Neural Networks for Continuous Prediction
- 设计新架构，解决传统方法在连续预测中的计算瓶颈。
- 在5个数据集上同时提升预测效率与准确率。
- 适合需要高频更新预测结果的实时系统场景。
时序图神经网络（TGNN）在处理动态图方面至关重要。然而，现有TGNN主要针对特定时间跨度的一次性预测，而许多实际应用需要在长时间内频繁发布预测结果。直接将现有TGNN应用于连续预测场景，会带来显著的计算开销或预测质量下降，尤其在大规模图上更为明显。本文重新审视TGNN中连续预测的挑战，提出{ extsc{Coden}}——一种专为动态图设计的高效且有效的TGNN模型。Coden创新性地克服了现有方法的关键复杂度瓶颈，同时保持相近的预测精度。我们进一步提供了理论分析，验证Coden的有效性与效率，并揭示其与基于RNN和注意力机制模型的双重关系。在五个动态数据集上的评估表明，Coden在效率和有效性两方面均超越现有基准，成为演化图环境中连续预测的更优解决方案。
原文摘要 · Abstract (English)
Temporal Graph Neural Networks (TGNNs) are pivotal in processing dynamic graphs. However, existing TGNNs primarily target one-time predictions for a given temporal span, whereas many practical applications require continuous predictions, that predictions are issued frequently over time. Directly adapting existing TGNNs to continuous-prediction scenarios introduces either significant computational overhead or prediction quality issues especially for large graphs. This paper revisits the challenge of { continuous predictions} in TGNNs, and introduces {\sc Coden}, a TGNN model designed for efficient and effective learning on dynamic graphs. {\sc Coden} innovatively overcomes the key complexity bottleneck in existing TGNNs while preserving comparable predictive accuracy. Moreover, we further provide theoretical analyses that substantiate the effectiveness and efficiency of {\sc Coden}, and clarify its duality relationship with both RNN-based and attention-based models. Our evaluations across five dynamic datasets show that {\sc Coden} surpasses existing performance benchmarks in both efficiency and effectiveness, establishing it as a superior solution for continuous prediction in evolving graph environments.
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