意识到不确定性的解动态图注意力网络,用于分布之外的泛化
IEEE transactions on pattern analysis and machine intelligence
|October 16, 2025
概括
本研究引入了一种用于动态图形神经网络 (DyGNN) 的新方法,以解决分布变化和模式不确定性. 拟议的信息瓶指导解动态图注意力网络 (IB-D2GAT) 有效地识别了稳定预测的不变模式.
科学领域:
- 图形神经网络的神经网络
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 人工智能的人工智能
背景情况:
- 动态图形神经网络 (DyGNN) 对于分析不断变化的图形结构和时间数据至关重要.
- 现实世界的动态图表经常显示分布变化和模式不确定性,挑战现有的DyGNNs.
- 当前的DyGNN在面对分布变化和不确定的模式时,在预测方面扎.
研究的目的:
- 通过发现和利用不变模式,开发一种处理动态图中的时空分布移动的方法.
- 在预测过程中考虑图形模式中的不确定性.
- 为了应对识别复杂的时空模式的挑战,并确保模式不确定性处理的理论保证.
主要方法:
- 提出了信息瓶引导的解动态图表注意力网络 (IB-D2GAT).
- 采用一个不纠的时空注意力机制来捕捉不变和变异的模式.
- 利用信息瓶原理与基于分布的不变性优化策略来注入随机性并防止变量模式的虚假影响.
主要成果:
- IB-D2GAT模型有效地处理动态图中的时空分布变化和不确定性.
- 拟议的不变性优化策略理论上可以确保准确识别具有稳定的预测能力的不变性模式.
- 实验证明了IB-D2GAT在分布转移下的真实世界和合成数据集上的优越性,而不是最新的基线.
结论:
- 在分布转移和模式不确定性的情况下,IB-D2GAT为动态图分析提供了强大的解决方案.
- 该方法发现和利用不变的时空模式的能力提供了稳定可靠的预测.
- 这项工作推进了动态图形学习领域,通过提供理论上有基础的和经验验证的分布转移适应方法.
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