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

翻译：图神经网络的一个主流范式基于消息传递框架。在此框架中，信息通信仅在相邻节点之间实现。采用此范式的方法面临的挑战在于确保节点间高效且准确的长距离通信，因为深层卷积网络容易出现过平滑问题。本文提出了一种基于时间导数图扩散（TIDE）的新方法，以克服消息传递框架的这些结构性局限。我们的方法能够在不同任务和网络通道上优化扩散的空间范围，从而有效实现中长距离通信。此外，我们证明了我们的架构设计还能实现局部消息传递，因此继承了局部消息传递方法的能力。在广泛使用的图基准测试以及合成网格和图数据集上，我们提出的框架以显著优势超越了现有最先进方法。