SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation

29 June 2023

Papers citing "SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation"

8 / 8 papers shown

Title
Diffusion on Graph: Augmentation of Graph Structure for Node Classification Yancheng Wang Changyu Liu Yingzhen Yang DiffM GNN 102 0 0 16 Mar 2025
Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? Xiang Wang Yang Liu Lexi Pang Siwei Chen Muhan Zhang DiffM 237 0 0 04 Feb 2025
Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models Xu Shen Yili Wang Kaixiong Zhou Shirui Pan Xin Wang 54 8 0 24 Apr 2024
Diffusion-based Graph Generative Methods Hongyang Chen Can Xu Lingyu Zheng Qiang Zhang Xuemin Lin DiffM MedIm 61 0 0 28 Jan 2024
Leveraging Graph Diffusion Models for Network Refinement Tasks Puja Trivedi Ryan Rossi David Arbour Tong Yu Franck Dernoncourt Sungchul Kim Nedim Lipka Namyong Park Nesreen K. Ahmed Danai Koutra DiffM 53 0 0 29 Nov 2023
Sparse Training of Discrete Diffusion Models for Graph Generation Yiming Qin Clément Vignac Pascal Frossard 36 13 0 03 Nov 2023
Diffusion Models for Graphs Benefit From Discrete State Spaces K. Haefeli Karolis Martinkus Nathanael Perraudin Roger Wattenhofer DiffM 106 54 0 04 Oct 2022
Deep Graph Generators: A Survey Faezeh Faez Yassaman Ommi M. Baghshah Hamid R. Rabiee GNN AI4CE 53 57 0 31 Dec 2020