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Recently developed deep neural models like NetGAN, CELL, and Variational Graph Autoencoders have made progress but face limitations in replicating key graph statistics on generating large graphs.
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Stochastic blockmodels: First steps
P. W. Holland, K. B. Laskey, and S. Leinhardt · 1983
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Assortative mixing in networks
M. E. Newman · 2002
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Random graph models of social networks
M. E. Newman, D. J. Watts, and S. H. Strogatz · 2002
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The political blogosphere and the 2004 us election: divided they blog
L. A. Adamic and N. Glance · 2005
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The biogrid interaction database: 2011 update
C. Stark, B.-J. Breitkreutz, A. Chatr-Aryamontri, L. Boucher, R. Oughtred, M. S. Livstone, J. Nixon, K. Van Auken, X. Wang, X. Shi, et al · 2010
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Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
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Variational graph auto-encoders
T. N. Kipf and M. Welling · 2016
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Netgan: Generating graphs via random walks
A. Bojchevski, O. Shchur, D. Zügner, and S. Günnemann · 2018
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Learning deep generative models of graphs
Y. Li, O. Vinyals, C. Dyer, R. Pascanu, and P. Battaglia · 2018
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GraphRNN: Generating realistic graphs with deep auto-regressive models
J. You, R. Ying, X. Ren, W. L. Hamilton, and J. Leskovec · 2018
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Efficient graph generation with graph recurrent attention networks
R. Liao, Y. Li, Y. Song, S. Wang, W. Hamilton, D. K. Duvenaud, R. Urtasun, and R. Zemel · 2019
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Graph normalizing flows
J. Liu, A. Kumar, J. Ba, J. Kiros, and K. Swersky · 2019
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Stochastic blockmodels meet graph neural networks
N. Mehta, L. C. Duke, and P. Rai · 2019
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Scalable deep generative modeling for sparse graphs
The impossibility of low-rank representations for triangle-rich complex networks
C. Seshadhri, A. Sharma, A. Stolman, and A. Goel · 2020
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Moflow: an invertible flow model for generating molecular graphs
C. Zang and F. Wang · 2020
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On the power of edge independent graph models
S. Chanpuriya, C. Musco, K. Sotiropoulos, and C. Tsourakakis · 2021
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Order matters: Probabilistic modeling of node sequence for graph generation
X. Chen, X. Han, J. Hu, F. J. Ruiz, and L. Liu · 2021
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Diffusion models for graphs benefit from discrete state spaces
K. K. Haefeli, K. Martinkus, N. Perraudin, and R. Wattenhofer · 2022
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H. Dai, A. Nazi, Y. Li, B. Dai, and D. Schuurmans · 2020
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Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
Cited alongside, same era.
Dirichlet graph variational autoencoder
J. Li, J. Yu, J. Li, H. Zhang, K. Zhao, Y. Rong, H. Cheng, and J. Huang · 2020
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Permutation invariant graph generation via score-based generative modeling
C. Niu, Y. Song, J. Song, S. Zhao, A. Grover, and S. Ermon · 2020
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Netgan without gan: From random walks to low-rank approximations
L. Rendsburg, H. Heidrich, and U. Von Luxburg · 2020
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Interpretable node representation with attribute decoding
X. Chen, X. Chen, and L. Liu
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Nvdiff: Graph generation through the diffusion of node vectors
X. Chen, Y. Li, A. Zhang, and L.-p. Liu
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J. Jo, S. Lee, and S. J. Hwang · 2022
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Digress: Discrete denoising diffusion for graph generation
C. Vignac, I. Krawczuk, A. Siraudin, B. Wang, V. Cevher, and P. Frossard · 2022
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Efficient and degree-guided graph generation via discrete diffusion modeling
X. Chen, J. He, X. Han, and L.-P. Liu · 2023
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Fitting autoregressive graph generative models through maximum likelihood estimation
X. Han, X. Chen, F. J. Ruiz, and L.-P. Liu · 2023
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