@inproceedings{
    southern2023curvature,
    title={Curvature Filtrations for Graph Generative Model Evaluation},
    author={Joshua Southern and Jeremy Wayland and Michael M. Bronstein and Bastian Rieck},
    booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
    year={2023},
    url={https://openreview.net/forum?id=Dt71xKyabn}
}

@misc{bazhenov2023evaluating,
      title={Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts}, 
      author={Gleb Bazhenov and Denis Kuznedelev and Andrey Malinin and Artem Babenko and Liudmila Prokhorenkova},
      year={2023},
      eprint={2302.13875},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{SohlDickstein2015DeepUL,
  title={Deep Unsupervised Learning using Nonequilibrium Thermodynamics},
  author={Jascha Narain Sohl-Dickstein and Eric A. Weiss and Niru Maheswaranathan and Surya Ganguli},
  journal={ArXiv},
  year={2015},
  volume={abs/1503.03585}
}

@article{Ho2020DenoisingDP,
  title={Denoising Diffusion Probabilistic Models},
  author={Jonathan Ho and Ajay Jain and P. Abbeel},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.11239}
}

@article{massart1990tight,
  title={The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality},
  author={Massart, Pascal},
  journal={The annals of Probability},
  pages={1269--1283},
  year={1990},
  publisher={JSTOR}
}

@article{Dhariwal2021DiffusionMB,
  title={Diffusion Models Beat GANs on Image Synthesis},
  author={Prafulla Dhariwal and Alex Nichol},
  journal={ArXiv},
  year={2021},
  volume={abs/2105.05233}
}

@article{Fan2023GenerativeDM,
  title={Generative Diffusion Models on Graphs: Methods and Applications},
  author={Wenqi Fan and Cheng-Bao Liu and Yunqing Liu and Jiatong Li and Hang Li and Hui Liu and Jiliang Tang and Qing Li},
  journal={ArXiv},
  year={2023},
  volume={abs/2302.02591}
}

@inproceedings{Niu2020PermutationIG,
  title={Permutation Invariant Graph Generation via Score-Based Generative Modeling},
  author={Chenhao Niu and Yang Song and Jiaming Song and Shengjia Zhao and Aditya Grover and Stefano Ermon},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  year={2020}
}

@article{Song2019GenerativeMB,
  title={Generative Modeling by Estimating Gradients of the Data Distribution},
  author={Yang Song and Stefano Ermon},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.05600}
}

@inproceedings{Jo2022ScorebasedGM,
  title={Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations},
  author={Jaehyeong Jo and Seul Lee and Sung Ju Hwang},
  booktitle={International Conference on Machine Learning},
  year={2022}
}

@article{Song2020ScoreBasedGM,
  title={Score-Based Generative Modeling through Stochastic Differential Equations},
  author={Yang Song and Jascha Narain Sohl-Dickstein and Diederik P. Kingma and Abhishek Kumar and Stefano Ermon and Ben Poole},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.13456}
}

@article{Jo2023GraphGW,
  title={Graph Generation with Destination-Driven Diffusion Mixture},
  author={Jaehyeong Jo and Dongki Kim and Sung Ju Hwang},
  journal={ArXiv},
  year={2023},
  volume={abs/2302.03596}
}

@article{Vignac2022DiGressDD,
  title={DiGress: Discrete Denoising diffusion for graph generation},
  author={Cl{\'e}ment Vignac and Igor Krawczuk and Antoine Siraudin and Bohan Wang and Volkan Cevher and Pascal Frossard},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.14734}
}

@article{Austin2021StructuredDD,
  title={Structured Denoising Diffusion Models in Discrete State-Spaces},
  author={Jacob Austin and Daniel D. Johnson and Jonathan Ho and Daniel Tarlow and Rianne van den Berg},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.03006}
}

@inproceedings{Hoogeboom2021ArgmaxFA,
  title={Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions},
  author={Emiel Hoogeboom and Didrik Nielsen and Priyank Jaini and Patrick Forr'e and Max Welling},
  booktitle={Neural Information Processing Systems},
  year={2021}
}


@InProceedings{pmlr-v162-hoogeboom22a,
  title = 	 {Equivariant Diffusion for Molecule Generation in 3{D}},
  author =       {Hoogeboom, Emiel and Satorras, V\'{\i}ctor Garcia and Vignac, Cl{\'e}ment and Welling, Max},
  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
  pages = 	 {8867--8887},
  year = 	 {2022},
  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
  volume = 	 {162},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {17--23 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v162/hoogeboom22a/hoogeboom22a.pdf},
  url = 	 {https://proceedings.mlr.press/v162/hoogeboom22a.html},
  abstract = 	 {This work introduces a diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations. Our E(3) Equivariant Diffusion Model (EDM) learns to denoise a diffusion process with an equivariant network that jointly operates on both continuous (atom coordinates) and categorical features (atom types). In addition, we provide a probabilistic analysis which admits likelihood computation of molecules using our model. Experimentally, the proposed method significantly outperforms previous 3D molecular generative methods regarding the quality of generated samples and the efficiency at training time.}
}

@article{Luo2022FastGG,
  title={Fast Graph Generation via Spectral Diffusion},
  author={Tianze Luo and Zhanfeng Mo and Sinno Jialin Pan},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.08892}
}

@article{Huang2022GraphGDPGD,
  title={GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation},
  author={Han Huang and Leilei Sun and Bowen Du and Yanjie Fu and Weifeng Lv},
  journal={2022 IEEE International Conference on Data Mining (ICDM)},
  year={2022},
  pages={201-210}
}

@misc{haefeli2022diffusion,
      title={Diffusion Models for Graphs Benefit From Discrete State Spaces}, 
      author={Kilian Konstantin Haefeli and Karolis Martinkus and Nathanaël Perraudin and Roger Wattenhofer},
      year={2022},
      eprint={2210.01549},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{Tseng2023GraphGUIDEIA,
  title={GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusion},
  author={Alex Tseng and Nathaniel Diamant and Tommaso Biancalani and Gabriele Scalia},
  journal={ArXiv},
  year={2023},
  volume={abs/2302.03790}
}

@article{Bao2022EquivariantES,
  title={Equivariant Energy-Guided SDE for Inverse Molecular Design},
  author={Fan Bao and Min Zhao and Zhongkai Hao and Pei‐Yun Li and Chongxuan Li and Jun Zhu},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.15408}
}

@article{Vignac2023MiDiMG,
  title={MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation},
  author={Cl{\'e}ment Vignac and Nagham Osman and Laura Toni and Pascal Frossard},
  journal={ArXiv},
  year={2023},
  volume={abs/2302.09048}
}

@article{Preuer2018FrchetCD,
  title={Fr{\'e}chet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery},
  author={Kristina Preuer and Philipp Renz and Thomas Unterthiner and Sepp Hochreiter and G{\"u}nter Klambauer},
  journal={Journal of chemical information and modeling},
  year={2018},
  volume={58 9},
  pages={
          1736-1741
        }
}

@inproceedings{Costa2010FastNS,
  title={Fast Neighborhood Subgraph Pairwise Distance Kernel},
  author={Fabrizio Costa and Kurt De Grave},
  booktitle={International Conference on Machine Learning},
  year={2010}
}

@inproceedings{
o'bray2022evaluation,
title={Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions},
author={Leslie O'Bray and Max Horn and Bastian Rieck and Karsten Borgwardt},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=tBtoZYKd9n}
}

@misc{thompson2022evaluation,
      title={On Evaluation Metrics for Graph Generative Models}, 
      author={Rylee Thompson and Boris Knyazev and Elahe Ghalebi and Jungtaek Kim and Graham W. Taylor},
      year={2022},
      eprint={2201.09871},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{Xu2018HowPA,
  title={How Powerful are Graph Neural Networks?},
  author={Keyulu Xu and Weihua Hu and Jure Leskovec and Stefanie Jegelka},
  journal={ArXiv},
  year={2018},
  volume={abs/1810.00826}
}

@misc{martinkus2022spectre,
      title={SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators}, 
      author={Karolis Martinkus and Andreas Loukas and Nathanaël Perraudin and Roger Wattenhofer},
      year={2022},
      eprint={2204.01613},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{
krawczuk2021gggan,
title={{\{}GG{\}}-{\{}GAN{\}}: A Geometric Graph Generative Adversarial Network},
author={Igor Krawczuk and Pedro Abranches and Andreas Loukas and Volkan Cevher},
year={2021},
url={https://openreview.net/forum?id=qiAxL3Xqx1o}
}

@misc{kipf2016variational,
      title={Variational Graph Auto-Encoders}, 
      author={Thomas N. Kipf and Max Welling},
      year={2016},
      eprint={1611.07308},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@article{Simonovskygraphvae,
  author       = {Martin Simonovsky and
                  Nikos Komodakis},
  title        = {GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders},
  journal      = {CoRR},
  volume       = {abs/1802.03480},
  year         = {2018},
  url          = {http://arxiv.org/abs/1802.03480},
  eprinttype    = {arXiv},
  eprint       = {1802.03480},
  timestamp    = {Mon, 13 Aug 2018 16:45:57 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1802-03480.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}   



@article{Mittongraphvae,
  author       = {Joshua Mitton and
                  Hans M. Senn and
                  Klaas Wynne and
                  Roderick Murray{-}Smith},
  title        = {A Graph {VAE} and Graph Transformer Approach to Generating Molecular
                  Graphs},
  journal      = {CoRR},
  volume       = {abs/2104.04345},
  year         = {2021},
  url          = {https://arxiv.org/abs/2104.04345},
  eprinttype    = {arXiv},
  eprint       = {2104.04345},
  timestamp    = {Tue, 13 Apr 2021 16:46:17 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2104-04345.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@misc{goodfellow2014generative,
      title={Generative Adversarial Networks}, 
      author={Ian J. Goodfellow and Jean Pouget-Abadie and Mehdi Mirza and Bing Xu and David Warde-Farley and Sherjil Ozair and Aaron Courville and Yoshua Bengio},
      year={2014},
      eprint={1406.2661},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@misc{kingma2022autoencoding,
      title={Auto-Encoding Variational Bayes}, 
      author={Diederik P Kingma and Max Welling},
      year={2022},
      eprint={1312.6114},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@misc{rezende2016variational,
      title={Variational Inference with Normalizing Flows}, 
      author={Danilo Jimenez Rezende and Shakir Mohamed},
      year={2016},
      eprint={1505.05770},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@misc{sohldickstein2015deep,
      title={Deep Unsupervised Learning using Nonequilibrium Thermodynamics}, 
      author={Jascha Sohl-Dickstein and Eric A. Weiss and Niru Maheswaranathan and Surya Ganguli},
      year={2015},
      eprint={1503.03585},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{ho2020denoising,
      title={Denoising Diffusion Probabilistic Models}, 
      author={Jonathan Ho and Ajay Jain and Pieter Abbeel},
      year={2020},
      eprint={2006.11239},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@inproceedings{Scott1992MultivariateDE,
  title={Multivariate Density Estimation: Theory, Practice, and Visualization},
  author={David W. Scott},
  booktitle={Wiley Series in Probability and Statistics},
  year={1992}
}



@article{Massey1951TheKT,
 ISSN = {01621459},
 URL = {http://www.jstor.org/stable/2280095},
 author = {Frank J. Massey},
 journal = {Journal of the American Statistical Association},
 number = {253},
 pages = {68--78},
 publisher = {[American Statistical Association, Taylor & Francis, Ltd.]},
 title = {The Kolmogorov-Smirnov Test for Goodness of Fit},
 urldate = {2024-05-31},
 volume = {46},
 year = {1951}
}

@inproceedings{KolmogorovSmirnov1933SullaDE,
  title={Sulla determinazione emp{\'i}rica di uma legge di distribuzione},
  author={AN Kolmogorov-Smirnov and Andrey Nikolaevich Kolmogorov and M Kolmogorov},
  year={1933}
}

@inproceedings{Kuiper1960TestsCR,
  title={Tests concerning random points on a circle},
  author={Nicolaas H. Kuiper},
  year={1960}
}

@article{Ramakrishnan2014QuantumCS,
  title={Quantum chemistry structures and properties of 134 kilo molecules},
  author={Raghunathan Ramakrishnan and Pavlo O. Dral and Matthias Rupp and O. Anatole von Lilienfeld},
  journal={Scientific Data},
  year={2014},
  volume={1}
}

@article{Prasanna2009TopologicalPS,
  title={Topological polar surface area: a useful descriptor in 2D-QSAR.},
  author={Sivaprakasam Prasanna and Robert J. Doerksen},
  journal={Current medicinal chemistry},
  year={2009},
  volume={16 1},
  pages={
          21-41
        }
}




@inproceedings{you2018graphrnn,
  author       = {Jiaxuan You and
                  Rex Ying and
                  Xiang Ren and
                  William L. Hamilton and
                  Jure Leskovec},
  editor       = {Jennifer G. Dy and
                  Andreas Krause},
  title        = {GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models},
  booktitle    = {Proceedings of the 35th International Conference on Machine Learning,
                  {ICML} 2018, Stockholmsm{\"{a}}ssan, Stockholm, Sweden, July
                  10-15, 2018},
  series       = {Proceedings of Machine Learning Research},
  volume       = {80},
  pages        = {5694--5703},
  publisher    = {{PMLR}},
  year         = {2018},
  url          = {http://proceedings.mlr.press/v80/you18a.html},
  timestamp    = {Mon, 01 Mar 2021 13:46:59 +0100},
  biburl       = {https://dblp.org/rec/conf/icml/YouYRHL18.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@book{monahan2011numerical, place={Cambridge}, edition={2}, series={Cambridge Series in Statistical and Probabilistic Mathematics}, title={Numerical Methods of Statistics}, DOI={10.1017/CBO9780511977176}, publisher={Cambridge University Press}, author={Monahan, John F.}, year={2011}, collection={Cambridge Series in Statistical and Probabilistic Mathematics}}

@article{Dwivedi&BressonGraphTrans ,
  author       = {Vijay Prakash Dwivedi and
                  Xavier Bresson},
  title        = {A Generalization of Transformer Networks to Graphs},
  journal      = {CoRR},
  volume       = {abs/2012.09699},
  year         = {2020},
  url          = {https://arxiv.org/abs/2012.09699},
  eprinttype    = {arXiv},
  eprint       = {2012.09699},
  timestamp    = {Sun, 03 Jan 2021 18:46:06 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2012-09699.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{goodfellow2020generative,
  title={Generative adversarial networks},
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  journal={Communications of the ACM},
  volume={63},
  number={11},
  pages={139--144},
  year={2020},
  publisher={ACM New York, NY, USA}
}

@article{LiaoGRANs,
  author       = {Renjie Liao and
                  Yujia Li and
                  Yang Song and
                  Shenlong Wang and
                  Charlie Nash and
                  William L. Hamilton and
                  David Duvenaud and
                  Raquel Urtasun and
                  Richard S. Zemel},
  title        = {Efficient Graph Generation with Graph Recurrent Attention Networks},
  journal      = {CoRR},
  volume       = {abs/1910.00760},
  year         = {2019},
  url          = {http://arxiv.org/abs/1910.00760},
  eprinttype    = {arXiv},
  eprint       = {1910.00760},
  timestamp    = {Thu, 01 Oct 2020 17:52:46 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1910-00760.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{segers2017empirical,
  title={The empirical beta copula},
  author={Segers, Johan and Sibuya, Masaaki and Tsukahara, Hideatsu},
  journal={Journal of Multivariate Analysis},
  volume={155},
  pages={35--51},
  year={2017},
  publisher={Elsevier}
}

@article{E-R-Model,
  title={On the Evolution of Random Graphs},
  author={Paul Erd{\H{o}}s and Alfr{\'e}d Rényi},
  year={1960},
  journal={Publicationes Mathematicae Debrecen},
  volume={6},
  pages={290-297}  
}
@book{dick2010digital,
  title={Digital Nets and Sequences: Discrepancy Theory and Quasi--Monte Carlo Integration},
  author={Dick, Josef and Pillichshammer, Friedrich},
  year={2010},
  publisher={Cambridge University Press}
}



@article{triangles_community,
  author       = {Christine Klymko and
                  David F. Gleich and
                  Tamara G. Kolda},
  title        = {Using Triangles to Improve Community Detection in Directed Networks},
  journal      = {CoRR},
  volume       = {abs/1404.5874},
  year         = {2014},
  url          = {http://arxiv.org/abs/1404.5874},
  eprinttype    = {arXiv},
  eprint       = {1404.5874},
  timestamp    = {Mon, 13 Aug 2018 16:48:53 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/KlymkoGK14.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{SMILE_art, 
author = {Hou, T. and Xia, K. and Zhang, W. and Xu, X.}, 
title = {Adme evaluation in drug discovery. 4. prediction of aqueous solubility based on atom contribution approach}, 
journal = {Journal of Chemical Information and Computer Sciences}, 
year = {2003}, 
volume = {44}, 
issue = {1}, 
pages = {266-275}, 
doi = {10.1021/ci034184n} 
}

@article{ERGraphs,
  title={On the Evolution of Random Graphs},
  author={Erdős, Paul and Rényi, Alfréd},
  journal={Publications of the Mathematical Institute of the Hungarian Academy of Sciences},
  volume={5},
  number={1},
  pages={17--61},
  year={1960},
  publisher={JSTOR}
}

@article{Arlot2009ASO,
  title={A survey of cross-validation procedures for model selection},
  author={Sylvain Arlot and Alain Celisse},
  journal={Statistics Surveys},
  year={2009},
  volume={4},
  pages={40-79},
  url={https://api.semanticscholar.org/CorpusID:14332192}
}

@article{Farahani2020ABR,
  title={A Brief Review of Domain Adaptation},
  author={Abolfazl Farahani and Sahar Voghoei and Khaled M. Rasheed and Hamid Reza Arabnia},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.03978},
  url={https://api.semanticscholar.org/CorpusID:222209143}
}

@inproceedings{Koh2020WILDSAB,
  title={WILDS: A Benchmark of in-the-Wild Distribution Shifts},
  author={Pang Wei Koh and Shiori Sagawa and Henrik Marklund and Sang Michael Xie and Marvin Zhang and Akshay Balsubramani and Weihua Hu and Michihiro Yasunaga and Richard Lanas Phillips and Irena Gao and Tony Lee and Etiene David and Ian Stavness and Wei Guo and Berton A. Earnshaw and Imran S. Haque and Sara Beery and Jure Leskovec and Anshul Kundaje and Emma Pierson and Sergey Levine and Chelsea Finn and Percy Liang},
  booktitle={International Conference on Machine Learning},
  year={2020},
  url={https://api.semanticscholar.org/CorpusID:229156320}
}

@inproceedings{Chen2021OrderMP,
  title={Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation},
  author={Xiaohui Chen and Xu Han and Jiajing Hu and Francisco J. R. Ruiz and Liping Liu},
  booktitle={International Conference on Machine Learning},
  year={2021},
  url={https://api.semanticscholar.org/CorpusID:235417091}
}

@inproceedings{Liao2019EfficientGG,
  title={Efficient Graph Generation with Graph Recurrent Attention Networks},
  author={Renjie Liao and Yujia Li and Yang Song and Shenlong Wang and Charlie Nash and William L. Hamilton and David Kristjanson Duvenaud and Raquel Urtasun and Richard S. Zemel},
  booktitle={Neural Information Processing Systems},
  year={2019},
  url={https://api.semanticscholar.org/CorpusID:202783404}
}

@article{Yu2012AnalysisOK,
  title={Analysis of Kernel Mean Matching under Covariate Shift},
  author={Yaoliang Yu and Csaba Szepesvari},
  journal={ArXiv},
  year={2012},
  volume={abs/1206.4650},
  url={https://api.semanticscholar.org/CorpusID:14008705}
}
@article{de2021adapt,
	  title={ADAPT: Awesome Domain Adaptation Python Toolbox},
	  author={de Mathelin, Antoine and Deheeger, Fran{\c{c}}ois and Richard, Guillaume and Mougeot, Mathilde and Vayatis, Nicolas},
	  journal={arXiv preprint arXiv:2107.03049},
	  year={2021}
	}

@inproceedings{Huang2006CorrectingSS,
  title={Correcting Sample Selection Bias by Unlabeled Data},
  author={Jiayuan Huang and Alex Smola and Arthur Gretton and Karsten M. Borgwardt and Bernhard Scholkopf},
  booktitle={Neural Information Processing Systems},
  year={2006},
  url={https://api.semanticscholar.org/CorpusID:70831}
}

@article{Wang2004ImageQA,
  title={Image quality assessment: from error visibility to structural similarity},
  author={Zhou Wang and Alan Conrad Bovik and Hamid R. Sheikh and Eero P. Simoncelli},
  journal={IEEE Transactions on Image Processing},
  year={2004},
  volume={13},
  pages={600-612},
  url={https://api.semanticscholar.org/CorpusID:207761262}
}

@article{Zhang2018TheUE,
  title={The Unreasonable Effectiveness of Deep Features as a Perceptual Metric},
  author={Richard Zhang and Phillip Isola and Alexei A. Efros and Eli Shechtman and Oliver Wang},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2018},
  pages={586-595},
  url={https://api.semanticscholar.org/CorpusID:4766599}
}

@inproceedings{NIPS2017_8a1d6947,
 author = {Heusel, Martin and Ramsauer, Hubert and Unterthiner, Thomas and Nessler, Bernhard and Hochreiter, Sepp},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium},
 url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/8a1d694707eb0fefe65871369074926d-Paper.pdf},
 volume = {30},
 year = {2017}
}

@article{16e7f618-c06b-3d10-8705-1086b218d827,
 ISSN = {01621459},
 URL = {http://www.jstor.org/stable/2280095},
 abstract = {The test is based on the maximum difference between an empirical and a hypothetical cumulative distribution. Percentage points are tabled, and a lower bound to the power function is charted. Confidence limits for a cumulative distribution are described. Examples are given. Indications that the test is superior to the chi-square test are cited.},
 author = {Frank J. Massey},
 journal = {Journal of the American Statistical Association},
 number = {253},
 pages = {68--78},
 publisher = {[American Statistical Association, Taylor & Francis, Ltd.]},
 title = {The Kolmogorov-Smirnov Test for Goodness of Fit},
 urldate = {2024-02-10},
 volume = {46},
 year = {1951}
}

#inproceedings{KolmogorovSmirnov1933SullaDE,
#  title={Sulla determinazione emp{\'i}rica di uma legge di distribuzione},
  author={AN Kolmogorov-Smirnov and Andrej Nikolajevi{\vc} Kolmogorov and M Kolmogorov},
  year={1933},
  url={https://api.semanticscholar.org/CorpusID:222427298}
}

@inproceedings{Heusel2017GANsTB,
  title={GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium},
  author={Martin Heusel and Hubert Ramsauer and Thomas Unterthiner and Bernhard Nessler and Sepp Hochreiter},
  booktitle={Neural Information Processing Systems},
  year={2017},
  url={https://api.semanticscholar.org/CorpusID:326772}
}

#article{Preuer2018FrchetCD,
#  title={Fr{\'e}chet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery},
  author={Kristina Preuer and Philipp Renz and Thomas Unterthiner and Sepp Hochreiter and G{\"u}nter Klambauer},
  journal={Journal of chemical information and modeling},
  year={2018},
  volume={58 9},
  pages={
          1736-1741
        },
  url={https://api.semanticscholar.org/CorpusID:51892387}
}

@inproceedings{Kong2023AutoregressiveDM,
  title={Autoregressive Diffusion Model for Graph Generation},
  author={Lingkai Kong and Jiaming Cui and Haotian Sun and Yuchen Zhuang and B. Aditya Prakash and Chao Zhang},
  booktitle={International Conference on Machine Learning},
  year={2023},
  url={https://api.semanticscholar.org/CorpusID:259299519}
}
