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In the realm of generative models for graphs, extensive research has been conducted.
On the evolution of random graphs
Paul Erdos, Alfréd Rényi, et al · 1960
Earlier work this paper cites.
Reducing the bandwidth of sparse symmetric matrices
E. Cuthill and J. McKee · 1969
Earlier work this paper cites.
Reverse-time diffusion equation models
Brian D.O. Anderson · 1982
Earlier work this paper cites.
Stochastic blockmodels: First steps
Paul W. Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt · 1983
Earlier work this paper cites.
Statistical mechanics of complex networks
Réka Albert and Albert-László Barabási · 2002
Earlier work this paper cites.
Distinguishing enzyme structures from non-enzymes without alignments
Paul Dobson and Andrew Doig · 2003
Earlier work this paper cites.
Fast unfolding of communities in large networks
Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre · 2008
Earlier work this paper cites.
Exploring network structure, dynamics, and function using networkx
Aric A. Hagberg, Daniel A. Schult, and Pieter J. Swart · 2008
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
Earlier work this paper cites.
Graph-based pattern-oriented, context-sensitive source code completion
Anh Tuan Nguyen, Tung Thanh Nguyen, Hoan Anh Nguyen, Ahmed Tamrawi, Hung Viet Nguyen, Jafar M. Al-Kofahi, and Tien Nhut Nguyen · 2012
Earlier work this paper cites.
Graph kernels for object category prediction in task-dependent robot grasping
Marion Neumann, Plinio Moreno, Laura Antanas, R. Garnett, and Kristian Kersting · 2013
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics, 2015
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Layer normalization, 2016
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
Earlier work this paper cites.
Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
Earlier work this paper cites.
Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
Earlier work this paper cites.
Graph reduction with spectral and cut guarantees
Andreas Loukas · 2018
Earlier work this paper cites.
Spectrally approximating large graphs with smaller graphs
Andreas Loukas and Pierre Vandergheynst · 2018
Earlier work this paper cites.
Graphvae: Towards generation of small graphs using variational autoencoders, 2018
Martin Simonovsky and Nikos Komodakis · 2018
Earlier work this paper cites.
Graphrnn: Generating realistic graphs with deep auto-regressive models, 2018
Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, and Jure Leskovec · 2018
Earlier work this paper cites.
Graph neural networks for social recommendation
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Yihong Eric Zhao, Jiliang Tang, and Dawei Yin · 2019
Earlier work this paper cites.
Fast graph representation learning with pytorch geometric, 2019
Matthias Fey and Jan Eric Lenssen · 2019
Earlier work this paper cites.
Graphite: Iterative generative modeling of graphs, 2019
Aditya Grover, Aaron Zweig, and Stefano Ermon · 2019
Earlier work this paper cites.
A unifying framework for spectrum-preserving graph sparsification and coarsening
Gecia Bravo Hermsdorff and Lee M. Gunderson · 2019
Cited alongside, same era.
Graph normalizing flows, 2019
Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, and Kevin Swersky · 2019
Cited alongside, same era.
How powerful are graph neural networks?, 2019
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Cited alongside, same era.
Learning to execute programs with instruction pointer attention graph neural networks
David Bieber, Charles Sutton, H. Larochelle, and Daniel Tarlow · 2020
Cited alongside, same era.
Scalable deep generative modeling for sparse graphs
Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, and Dale Schuurmans · 2020
Cited alongside, same era.
Graphgen: A scalable approach to domain-agnostic labeled graph generation
Nikhil Goyal, Harsh Vardhan Jain, and Sayan Ranu · 2020
Sign and basis invariant networks for spectral graph representation learning, 2022
Derek Lim, Joshua Robinson, Lingxiao Zhao, Tess Smidt, Suvrit Sra, Haggai Maron, and Stefanie Jegelka · 2022
Later among the works it cites.
Spectre: Spectral conditioning helps to overcome the expressivity limits of one-shot graph generators, 2022
Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, and Roger Wattenhofer · 2022
Later among the works it cites.
Td-gen: Graph generation with tree decomposition, 2022
Hamed Shirzad, Hossein Hajimirsadeghi, Amir H. Abdi, and Greg Mori · 2022
Later among the works it cites.
Improved vector quantized diffusion models
Zhicong Tang, Shuyang Gu, Jianmin Bao, Dong Chen, and Fang Wen · 2022
Later among the works it cites.
Musiclm: Generating music from text
Andrea Agostinelli, Timo I Denk, Zalán Borsos, Jesse Engel, Mauro Verzetti, Antoine Caillon, Qingqing Huang, Aren Jansen, Adam Roberts, Marco Tagliasacchi, et al · 2023
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Cited alongside, same era.
Denoising diffusion probabilistic models, 2020
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Strategies for pre-training graph neural networks, 2020
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2020
Cited alongside, same era.
Efficient graph generation with graph recurrent attention networks, 2020
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, and Richard S. Zemel · 2020
Cited alongside, same era.
Provably powerful graph networks, 2020
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman · 2020
Cited alongside, same era.
Permutation invariant graph generation via score-based generative modeling, 2020
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, and Stefano Ermon · 2020
Cited alongside, same era.
Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
Cited alongside, same era.
Closest in time.
Discovering graph generation algorithms
Mihai Babiac, Karolis Martinkus, and Roger Wattenhofer · 2023
Closest in time.
Audiolm: a language modeling approach to audio generation
Zalán Borsos, Raphaël Marinier, Damien Vincent, Eugene Kharitonov, Olivier Pietquin, Matt Sharifi, Dominik Roblek, Olivier Teboul, David Grangier, Marco Tagliasacchi, et al · 2023
Closest in time.
Size matters: Large graph generation with higgs, 2023
Alex O. Davies, Nirav S. Ajmeri, and Telmo M. Silva Filho · 2023
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Improving graph generation by restricting graph bandwidth, 2023
Nathaniel Diamant, Alex M. Tseng, Kangway V. Chuang, Tommaso Biancalani, and Gabriele Scalia · 2023
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Unicats: A unified context-aware text-to-speech framework with contextual vq-diffusion and vocoding
Chenpeng Du, Yiwei Guo, Feiyu Shen, Zhijun Liu, Zheng Liang, Xie Chen, Shuai Wang, Hui Zhang, and Kai Yu · 2023
Closest in time.
Automating rigid origami design
Jeremia Geiger, Karolis Martinkus, Oliver Richter, and Roger Wattenhofer · 2023
Closest in time.
An unpooling layer for graph generation, 2023
Yinglong Guo, Dongmian Zou, and Gilad Lerman · 2023
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Diffusion models for graphs benefit from discrete state spaces, 2023
Kilian Konstantin Haefeli, Karolis Martinkus, Nathanaël Perraudin, and Roger Wattenhofer · 2023
Closest in time.
Graph generation with destination-predicting diffusion mixture
Jaehyeong Jo, Dongki Kim, and Sung Ju Hwang · 2023
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Higen: Hierarchical graph generative networks, 2023
Mahdi Karami · 2023
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Autoregressive diffusion model for graph generation, 2023
Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B. Aditya Prakash, and Chao Zhang · 2023
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Featured graph coarsening with similarity guarantees
Manoj Kumar, Anurag Sharma, Shashwat Saxena, and Surinder Kumar · 2023
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Sagess: Sampling graph denoising diffusion model for scalable graph generation, 2023
Stratis Limnios, Praveen Selvaraj, Mihai Cucuringu, Carsten Maple, Gesine Reinert, and Andrew Elliott · 2023
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Abdiffuser: Full-atom generation of in-vitro functioning antibodies
Karolis Martinkus, Jan Ludwiczak, Kyunghyun Cho, Weishao Lian, Julien Lafrance-Vanasse, Isidro Hotzel, Arvind Rajpal, Y. Wu, Richard Bonneau, Vladimir Gligorijević, and Andreas Loukas · 2023
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Attention is all you need, 2023
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2023
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Swingnn: Rethinking permutation invariance in diffusion models for graph generation, 2023
Qi Yan, Zhengyang Liang, Yang Song, Renjie Liao, and Lele Wang · 2023
Closest in time.