Jun 21, 2021 · We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as …

Feb 6, 2023 · Then, we summarize the major applications of generative diffusion models on graphs with a specific focus on molecule and protein modeling. Finally, we discuss promising …

Jan 4, 2025 · At its core, DiffGraph features a sophisticated latent heterogeneous graph diffusion mechanism, implementing a novel forward and backward diffusion process for superior noise …

Jan 25, 2025 · In this work, we introduced adaptive graph diffusion networks (AGDNs) to tackle challenges posed by deep GNNs in numerous applications. The approach adopted by AGDNs …

Diffusion Graph Nets (DGNs) enable direct sampling of these states via flow matching or diffusion -based denoising, given a mesh discretization of the system and its physical parameters. This …

In this work, we remove the restriction of using only the direct neighbors by introducing a powerful, yet spatially localized graph convolution: Graph diffusion convolution (GDC). GDC leverages …

We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data. Through the introduction of a diffusion-convolution oper-ation, we show how diffusion …

In this paper, we propose Graph Diffusion-Embedding Networks (GDENs) for graph data representation and learn-ing. GDENs are motivated by our development on graph based …

Therefore, this paper proposes a new framework: DD-GNN for graph neural networks with joint diffusion model. In this paper, the computational overhead is reduced by ignoring the structural …

Oct 27, 2023 · To overcome these limitations, we propose a novel framework called the Self-Contrastive Graph Diffusion Network (SCGDN). Our framework consists of two main …