论文标题
潮汐：图表上深度学习的时间派生扩散
TIDE: Time Derivative Diffusion for Deep Learning on Graphs
论文作者
论文摘要
图形神经网络的突出范式基于消息通信框架。在此框架中，信息通信仅在相邻节点之间实现。使用这种范式的方法的挑战是确保节点之间的有效，准确的长距离通信，因为深卷积网络容易过度平滑。在本文中，我们提出了一种基于时间衍生图扩散（TIDE）的新方法，以克服消息通话框架的这些结构局限性。我们的方法允许优化各种任务和网络通道之间扩散的空间范围，从而有效地实现了媒介和长距离通信。此外，我们表明我们的体系结构设计还可以实现本地消息，从而从本地消息通话方法的功能中继承。我们表明，在广泛使用的图基准和合成网格和图形数据集上，所提出的框架优于最先进的方法
A prominent paradigm for graph neural networks is based on the message-passing framework. In this framework, information communication is realized only between neighboring nodes. The challenge of approaches that use this paradigm is to ensure efficient and accurate long-distance communication between nodes, as deep convolutional networks are prone to oversmoothing. In this paper, we present a novel method based on time derivative graph diffusion (TIDE) to overcome these structural limitations of the message-passing framework. Our approach allows for optimizing the spatial extent of diffusion across various tasks and network channels, thus enabling medium and long-distance communication efficiently. Furthermore, we show that our architecture design also enables local message-passing and thus inherits from the capabilities of local message-passing approaches. We show that on both widely used graph benchmarks and synthetic mesh and graph datasets, the proposed framework outperforms state-of-the-art methods by a significant margin
