1. SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation
    Qi Yan, Zhengyang Liang, Yang Song, Renjie Liao, Lele Wang
    arXiv 2023. Paper   Github  
    2023-07-04
    2023-07-04
  2. SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation
    Stratis Limnios, Praveen Selvaraj, Mihai Cucuringu, Carsten Maple, Gesine Reinert, Andrew Elliott
    arXiv 2023. Paper  
    2023-06-29
    2023-06-29
  3. Complexity-aware Large Scale Origin-Destination Network Generation via Diffusion Model
    Can Rong, Jingtao Ding, Zhicheng Liu, Yong Li
    arXiv 2023. Paper  
    2023-06-08
    2023-06-08
  4. A Diffusion Model for Event Skeleton Generation
    Fangqi Zhu, Lin Zhang, Jun Gao, Bing Qin, Ruifeng Xu, Haiqin Yang
    arXiv 2023. Paper   Github  
    2023-05-27
    2023-05-27
  5. Confidence-Based Feature Imputation for Graphs with Partially Known Features
    Daeho Um, Jiwoong Park, Seulki Park, Jin Young Choi
    ICLR 2023. Paper   Github  
    2023-05-26
    2023-05-26
  6. Spatio-temporal Diffusion Point Processes
    Yuan Yuan, Jingtao Ding, Chenyang Shao, Depeng Jin, Yong Li
    arXiv 2023. Paper   Github  
    2023-05-21
    2023-05-21
  7. Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
    Xiaohui Chen, Jiaxing He, Xu Han, Li-Ping Liu
    ICML 2023. Paper  
    2023-05-06
    2023-05-06
  8. A 2D Graph-Based Generative Approach For Exploring Transition States Using Diffusion Model
    Seonghwan Kim, Jeheon Woo, Woo Youn Kim
    arXiv 2023. Paper  
    2023-04-20
    2023-04-20
  9. Two-stage Denoising Diffusion Model for Source Localization in Graph Inverse Problems
    Bosong Huang, Weihao Yu, Ruzhong Xie, Jing Xiao, Jin Huang
    arXiv 2023. Paper  
    2023-04-18
    2023-04-18
  10. Diffusion Probabilistic Models for Graph-Structured Prediction
    Hyosoon Jang, Sangwoo Mo, Sungsoo Ahn
    arXiv 2023. Paper  
    2023-02-21
    2023-02-21
  11. Graph Generation with Destination-Driven Diffusion Mixture
    Jaehyeong Jo, Dongki Kim, Sung Ju Hwang
    arXiv 2023. Paper  
    2023-02-07
    2023-02-07
  12. GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusion
    Alex M. Tseng, Nathaniel Diamant, Tommaso Biancalani, Gabriele Scalia
    arXiv 2023. Paper  
    2023-02-07
    2023-02-07
  13. DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models
    Haomin Wen, Youfang Lin, Yutong Xia, Huaiyu Wan, Roger Zimmermann, Yuxuan Liang
    arXiv 2023. Paper  
    2023-01-31
    2023-01-31
  14. DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion
    Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, Junchi Yan
    ICLR 2023. Paper  
    2023-01-23
    2023-01-23
  15. GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation
    Han Huang, Leilei Sun, Bowen Du, Yanjie Fu, Weifeng Lv
    IEEE ICDM 2022. Paper   Github  
    2022-12-04
    2022-12-04
  16. NVDiff: Graph Generation through the Diffusion of Node Vectors
    Cristian Sbrolli, Paolo Cudrano, Matteo Frosi, Matteo Matteucci
    arXiv 2022. Paper  
    2022-11-20
    2022-11-20
  17. Fast Graph Generative Model via Spectral Diffusion
    Tianze Luo, Zhanfeng Mo, Sinno Jialin Pan
    arXiv 2022. Paper  
    2022-11-16
    2022-11-16
  18. Diffusion Models for Graphs Benefit From Discrete State Spaces
    Kilian Konstantin Haefeli, Karolis Martinkus, Nathanaël Perraudin, Roger Wattenhofer
    NeurIPS Workshop 2022. Paper  
    2022-10-04
    2022-10-04
  19. DiGress: Discrete Denoising diffusion for graph generation
    Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, Pascal Frossard
    ICLR 2023. Paper  
    2022-09-29
    2022-09-29
  20. Permutation Invariant Graph Generation via Score-Based Generative Modeling
    Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
    AISTATS 2021. Paper   Github  
    2020-03-02
    2020-03-02
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