Please see our paper for more details: GREAD: Graph Neural Reaction-Diffusion Networks We will update more information of the code soon. Reaction-diffusion on a grid graph Diffusion on a grid network ...

Recently, fast and furious changes come to existence in various fields. In this paper, we identify a state before a change and the other state after the change as two stable stationary states of ...

Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations Jaehyeong Jo, Seul Lee, Sung Ju Hwang ICML, 2022. [Paper] [Github] 5 Feb 2022 Fast Graph Generative Model ...

Traffic prediction is the cornerstone of the intelligent transportation system (ITS), and accurate prediction is essential for planning route, alleviating traffic pressure, and optimizing public ...

Diffusion processes, characterised through operators such as the graph Laplacian, foster the development of diffusion maps which yield robust, multi-scale representations.

The success of graph neural networks (GNNs) largely relies on the process of aggregating information from neighbors defined by the input graph structures. Notably, message passing based GNNs, e.g., ...

In a one-dimensional advection-diffusion equation with temporally dependent coefficients three cases may arise: solute dispersion parameter is time dependent while the flow domain transporting the ...