Graph Generative Model for Benchmarking Graph Neural Networks

10 July 2022

Papers citing "Graph Generative Model for Benchmarking Graph Neural Networks"

7 / 7 papers shown

Title
LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation Mufei Li Viraj Shitole Eli Chien Changhai Man Zhaodong Wang Srinivas Sridharan Ying Zhang Tushar Krishna P. Li 53 0 0 04 Nov 2024
GraphMaker: Can Diffusion Models Generate Large Attributed Graphs? Mufei Li Eleonora Kreacic Vamsi K. Potluru Pan Li DiffM 44 7 0 20 Oct 2023
GraphWorld: Fake Graphs Bring Real Insights for GNNs John Palowitch Anton Tsitsulin Brandon Mayer Bryan Perozzi GNN 201 68 0 28 Feb 2022
IGLU: Efficient GCN Training via Lazy Updates S. Narayanan Aditya Sinha Prateek Jain Purushottam Kar Sundararajan Sellamanickam BDL 52 11 0 28 Sep 2021
Releasing Graph Neural Networks with Differential Privacy Guarantees Iyiola E. Olatunji Thorben Funke Megha Khosla 42 45 0 18 Sep 2021
GraphDF: A Discrete Flow Model for Molecular Graph Generation Youzhi Luo Keqiang Yan Shuiwang Ji DRL 185 188 0 01 Feb 2021
Junction Tree Variational Autoencoder for Molecular Graph Generation Wengong Jin Regina Barzilay Tommi Jaakkola 242 1,340 0 12 Feb 2018