ScaDyG:大规模动态图形学习的新范式
IEEE transactions on neural networks and learning systems
|January 12, 2026
概括
在ScaDyG中,通过重构拓和使用时间编码,为动态图 (DGs) 引入了一个可扩展的学习范式. 这种方法提高了下游任务的效率和性能,解决了动态图形神经网络 (DGNN) 的可扩展性问题.
科学领域:
- 机器学习 机器学习
- 图形神经网络的神经网络
- 动态系统 动态系统
背景情况:
- 动态图 (DGs) 模拟时间演变的关系,对于许多现实应用来说至关重要.
- 现有的动态图形神经网络 (DGNNs) 由于历史数据的增长而面临可扩展性挑战.
- 工业应用需要对下游任务进行高效的GD编码.
研究的目的:
- 提出ScaDyG,一个新的动态图的时间意识可扩展的学习范式.
- 解决传统 DGNN 的可扩展性限制.
- 提高GD编码下游任务的效率和性能.
主要方法:
- 时间感知拓重构 (TTR):将历史交互细分为时间步骤,以实现无重量,时间感知的传播.
- 动态时间编码 (DTE):使用指数函数集成精细的时间编码.
- 超级网络驱动的消息聚合:使用超级网络来实现节点表示的适应性时间融合.
主要成果:
- 在12个数据集上,ScaDyG在12个数据集上展示了与最先进的 (SOTA) 方法相比或更高的性能.
- 在节点级和链接级下游任务中取得了强的结果.
- 与现有方法相比,展示了更少的可学习参数和更高的计算效率.
结论:
- ScaDyG为学习动态图形提供了有效和高效的解决方案.
- 提出的方法成功地解决了DGNN中的可扩展性问题.
- 该方法为现实世界GD应用提供了一个有希望的方向.
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