Deep-Graph-Sprints：连续时间动态图中的加速表示学习
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs
AI总结：
针对连续时间动态图（CTDG）表示学习中的高推理延迟问题，提出Deep-Graph-Sprints（DGS）架构，在五个数据集上以4-12倍推理加速取得与SOTA相当的性能，弥合了深度表示学习与低延迟应用间的差距。
摘要：
连续时间动态图（CTDG）对于建模相互关联且不断演化的系统至关重要。从这些图中提取知识的传统方法通常依赖于特征工程或深度学习。特征工程受限于手工构建特征耗时且费力，而深度学习方法则面临高推理延迟，使其难以应用于实时场景。本文提出了Deep-Graph-Sprints（DGS），一种新颖的深度学习架构，专为在具有低延迟推理需求的CTDG上进行高效表示学习而设计。我们使用五个不同的数据集，将DGS与最先进（SOTA）的特征工程和图神经网络方法进行了基准测试。结果表明，DGS在取得具有竞争力的性能的同时，在我们的基准数据集上，与其他深度学习方法相比，推理速度提升了4倍至12倍。我们的方法有效地弥合了深度表示学习与CTDG低延迟应用需求之间的差距。
英文摘要：
Continuous-time dynamic graphs (CTDGs) are essential for modeling interconnected, evolving systems. Traditional methods for extracting knowledge from these graphs often depend on feature engineering or deep learning. Feature engineering is limited by the manual and time-intensive nature of crafting features, while deep learning approaches suffer from high inference latency, making them impractical for real-time applications. This paper introduces Deep-Graph-Sprints (DGS), a novel deep learning architecture designed for efficient representation learning on CTDGs with low-latency inference requirements. We benchmark DGS against state-of-the-art (SOTA) feature engineering and graph neural network methods using five diverse datasets. The results indicate that DGS achieves competitive performance while inference speed improves between 4x and 12x compared to other deep learning approaches on our benchmark datasets. Our method effectively bridges the gap between deep representation learning and low-latency application requirements for CTDGs.
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