TIDE: Time Derivative Diffusion for Deep Learning on Graphs

12/05/2022
by   Maximilian Krahn, et al.
0

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 over-smoothing. In this paper, we present a novel method based on time derivative graph diffusion (TIDE), with a learnable time parameter. Our approach allows to adapt the spatial extent of diffusion across different tasks and network channels, thus enabling medium and long-distance communication efficiently. Furthermore, we show that our architecture directly enables local message passing and thus inherits from the expressive power of local message passing approaches. We show that on widely used graph benchmarks we achieve comparable performance and on a synthetic mesh dataset we outperform state-of-the-art methods like GCN or GRAND by a significant margin.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset