  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
