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Graph generative models are essential across diverse scientific domains by capturing complex distributions over relational data.
Sur le probleme des courbes gauches en topologie
Kuratowski, C · 1930
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A general method for numerically simulating the stochastic time evolution of coupled chemical reactions
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Exact stochastic simulation of coupled chemical reactions
Gillespie, D. T · 1977
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Long short-term memory
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Approximate accelerated stochastic simulation of chemically reacting systems
Gillespie, D. T · 2001
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Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Zinc 15–ligand discovery for everyone
Sterling, T. and Irwin, J. J · 2015
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Variational graph auto-encoders
Kipf, T. N. and Welling, M · 2016
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Molgan: An implicit generative model for small molecular graphs
De Cao, N. and Kipf, T · 2018
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Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
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Constrained graph variational autoencoders for molecule design
Liu, Q., Allamanis, M., Brockschmidt, M., and Gaunt, A · 2018
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Moleculenet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., and Pande, V · 2018
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Graphrnn: Generating realistic graphs with deep auto-regressive models
You, J., Ying, R., Ren, X., Hamilton, W., and Leskovec, J · 2018
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Guacamol: benchmarking models for de novo molecular design
Brown, N., Fiscato, M., Segler, M. H., and Vaucher, A. C · 2019
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Efficient graph generation with graph recurrent attention networks
Liao, R., Li, Y., Song, Y., Wang, S., Hamilton, W., Duvenaud, D. K., Urtasun, R., and Zemel, R · 2019
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Graph normalizing flows
Liu, J., Kumar, A., Ba, J., Kiros, J., and Swersky, K · 2019
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Graphnvp: An invertible flow model for generating molecular graphs
Madhawa, K., Ishiguro, K., Nakago, K., and Abe, M · 2019
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Chembl: towards direct deposition of bioassay data
Mendez, D., Gaulton, A., Bento, A. P., Chambers, J., De Veij, M., Félix, E., Magariños, M. P., Mosquera, J. F., Mutowo, P., Nowotka, M., et al · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., and Grohe, M · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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Scalable deep generative modeling for sparse graphs
Dai, H., Nazi, A., Li, Y., Dai, B., and Schuurmans, D · 2020
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Graphgen: A scalable approach to domain-agnostic labeled graph generation
Goyal, N., Jain, H. V., and Ranu, S · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Quantifying explainers of graph neural networks in computational pathology
Jaume, G., Pati, P., Bozorgtabar, B., Foncubierta-Rodríguez, A., Feroce, F., Anniciello, A. M., Rau, T. T., Thiran, J.-P., Gabrani, M., and Goksel, O · 2020
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Compressed graph representation for scalable molecular graph generation
Kwon, Y., Lee, D., Choi, Y.-S., Shin, K., and Kang, S · 2020
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Permutation invariant graph generation via score-based generative modeling
Niu, C., Song, Y., Song, J., Zhao, S., Grover, A., and Ermon, S · 2020
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Molecular sets (moses): a benchmarking platform for molecular generation models
Polykovskiy, D., Zhebrak, A., Sanchez-Lengeling, B., Golovanov, S., Tatanov, O., Belyaev, S., Kurbanov, R., Artamonov, A., Aladinskiy, V., Veselov, M., et al · 2020
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Structured denoising diffusion models in discrete state-spaces
Austin, J., Johnson, D. D., Ho, J., Tarlow, D., and Van Den Berg, R · 2021
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2021
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Gotta go fast when generating data with score-based models
Jolicoeur-Martineau, A., Li, K., Piché-Taillefer, R., Kachman, T., and Mitliagkas, I · 2021
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Graph networks for molecular design
Mercado, R., Rastemo, T., Lindelöf, E., Klambauer, G., Engkvist, O., Chen, H., and Bjerrum, E. J · 2021
Tertiary lymphoid structures generation through graph-based diffusion
Madeira, M., Thanou, D., and Frossard, P · 2023
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Sparse training of discrete diffusion models for graph generation
Qin, Y., Vignac, C., and Frossard, P · 2023
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Score-based continuous-time discrete diffusion models
Sun, H., Yu, L., Dai, B., Schuurmans, D., and Dai, H · 2023
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Complex preferences for different convergent priors in discrete graph diffusion
Tseng, A. M., Diamant, N., Biancalani, T., and Scalia, G · 2023
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Midi: Mixed graph and 3d denoising diffusion for molecule generation
Vignac, C., Osman, N., Toni, L., and Frossard, P · 2023
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Restart sampling for improving generative processes
Xu, Y., Deng, M., Cheng, X., Tian, Y., Liu, Z., and Jaakkola, T · 2023
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A continuous time framework for discrete denoising models
Campbell, A., Benton, J., De Bortoli, V., Rainforth, T., Deligiannidis, G., and Doucet, A · 2022
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Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction
Chung, H., Sim, B., and Ye, J. C · 2022
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Score-based generative modeling with critically-damped langevin diffusion
Dockhorn, T., Vahdat, A., and Kreis, K · 2022
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Diffusion models for graphs benefit from discrete state spaces
Haefeli, K. K., Martinkus, K., Perraudin, N., and Wattenhofer, R · 2022
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Score-based generative modeling of graphs via the system of stochastic differential equations
Jo, J., Lee, S., and Hwang, S. J · 2022
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Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
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Fast sampling of diffusion models with exponential integrator
Zhang, Q. and Chen, Y · 2023
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On structural expressive power of graph transformers
Zhu, W., Wen, T., Song, G., Wang, L., and Zheng, B · 2023
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Multi-conditioned graph diffusion for neural architecture search
Asthana, R., Conrad, J., Dawoud, Y., Ortmanns, M., and Belagiannis, V · 2024
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Generative flows on discrete state-spaces: Enabling multimodal flows with applications to protein co-design
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Variational flow matching for graph generation
Eijkelboom, F., Bartosh, G., Andersson Naesseth, C., Welling, M., and van de Meent, J.-W · 2024
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Scaling rectified flow transformers for high-resolution image synthesis
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Discrete flow matching
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Retrobridge: Modeling retrosynthesis with markov bridges
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Efficient 3d molecular generation with flow matching and scale optimal transport
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Graph generation with diffusion mixture
Jo, J., Kim, D., and Hwang, S. J · 2024
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Advancing graph generation through beta diffusion
Liu, X., He, Y., Chen, B., and Zhou, M · 2024
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Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers
Ma, N., Goldstein, M., Albergo, M. S., Boffi, N. M., Vanden-Eijnden, E., and Xie, S · 2024
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Generative modelling of structurally constrained graphs
Madeira, M., Vignac, C., Thanou, D., and Frossard, P · 2024
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Stay on topic with classifier-free guidance
Sanchez, G., Spangher, A., Fan, H., Levi, E., and Biderman, S · 2024
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Cometh: A continuous-time discrete-state graph diffusion model
Siraudin, A., Malliaros, F. D., and Morris, C · 2024
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Difusco: Graph-based diffusion solvers for combinatorial optimization
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Discrete-state continuous-time diffusion for graph generation
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Graph denoising diffusion for inverse protein folding
Yi, K., Zhou, B., Shen, Y., Liò, P., and Wang, Y · 2024
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Unlocking guidance for discrete state-space diffusion and flow models
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