Fetching the paper…
Reading the bibliography…
Generating graphs from a target distribution is a significant challenge across many domains, including drug discovery and social network analysis.
On the evolution of random graphs
Paul Erdős, Alfréd Rényi, et al · 1960
Earlier work this paper cites.
Reducing the bandwidth of sparse symmetric matrices
Elizabeth Cuthill and James McKee · 1969
Earlier work this paper cites.
Off-line dictionary-based compression
N Jesper Larsson and Alistair Moffat · 2000
Earlier work this paper cites.
Statistical mechanics of complex networks
Réka Albert and Albert-László Barabási · 2002
Earlier work this paper cites.
Representing web graphs
Sriram Raghavan and Hector Garcia-Molina · 2003
Earlier work this paper cites.
Sparse matrix reordering algorithms for cluster identification
Chris Mueller · 2004
Earlier work this paper cites.
Brenda, the enzyme database: updates and major new developments
Ida Schomburg, Antje Chang, Christian Ebeling, Marion Gremse, Christian Heldt, Gregor Huhn, and Dietmar Schomburg · 2004
Earlier work this paper cites.
Permuting web and social graphs
Paolo Boldi, Massimo Santini, and Sebastiano Vigna · 2009
Earlier work this paper cites.
k2-trees for compact web graph representation
Nieves R Brisaboa, Susana Ladra, and Gonzalo Navarro · 2009
Earlier work this paper cites.
Zinc: a free tool to discover chemistry for biology
John J Irwin, Teague Sterling, Michael M Mysinger, Erin S Bolstad, and Ryan G Coleman · 2012
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Survey and taxonomy of lossless graph compression and space-efficient graph representations
Maciej Besta and Torsten Hoefler · 2018
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.
Learning deep generative models of graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia · 2018
Earlier work this paper cites.
Generating focused molecule libraries for drug discovery with recurrent neural networks
Marwin HS Segler, Thierry Kogej, Christian Tyrchan, and Mark P Waller · 2018
Earlier work this paper cites.
Self-attention with relative position representations
Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani · 2018
Cited alongside, same era.
Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
Cited alongside, same era.
Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec · 2018
Cited alongside, same era.
Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec · 2018
Cited alongside, same era.
Graphite: Iterative generative modeling of graphs
Aditya Grover, Aaron Zweig, and Stefano Ermon · 2019
Cited alongside, same era.
Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2020
Later among the works it cites.
Reverse logistics network design for effective management of medical waste in epidemic outbreaks: Insights from the coronavirus disease 2019 (covid-19) outbreak in wuhan (china)
Hao Yu, Xu Sun, Wei Deng Solvang, and Xu Zhao · 2020
Later among the works it cites.
Moflow: an invertible flow model for generating molecular graphs
Chengxi Zang and Fei Wang · 2020
Later among the works it cites.
Partition and code: learning how to compress graphs
Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias, and Michael Bronstein · 2021
Later among the works it cites.
Graphebm: Molecular graph generation with energy-based models
Meng Liu, Keqiang Yan, Bora Oztekin, and Shuiwang Ji · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mario Krenn, Florian Häse, A Nigam, Pascal Friederich, and Alán Aspuru-Guzik · 2019
Cited alongside, same era.
Efficient graph generation with graph recurrent attention networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Will Hamilton, David K Duvenaud, Raquel Urtasun, and Richard Zemel · 2019
Cited alongside, same era.
Graph normalizing flows
Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, and Kevin Swersky · 2019
Cited alongside, same era.
Graphnvp: An invertible flow model for generating molecular graphs
Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago, and Motoki Abe · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Vertex reordering for real-world graphs and applications: An empirical evaluation
Reet Barik, Marco Minutoli, Mahantesh Halappanavar, Nathan R Tallent, and Ananth Kalyanaraman · 2020
Cited alongside, same era.
Graphgen: a scalable approach to domain-agnostic labeled graph generation
Nikhil Goyal, Harsh Vardhan Jain, and Sayan Ranu · 2020
Cited alongside, same era.
Later among the works it cites.
Graphdf: A discrete flow model for molecular graph generation
Youzhi Luo, Keqiang Yan, and Shuiwang Ji · 2021
Later among the works it cites.
Hit and lead discovery with explorative rl and fragment-based molecule generation
Soojung Yang, Doyeong Hwang, Seul Lee, Seongok Ryu, and Sung Ju Hwang · 2021
Later among the works it cites.
Spanning tree-based graph generation for molecules
Sungsoo Ahn, Binghong Chen, Tianzhe Wang, and Le Song · 2022
Later among the works it cites.
Score-based generative modeling of graphs via the system of stochastic differential equations
Jaehyeong Jo, Seul Lee, and Sung Ju Hwang · 2022
Later among the works it cites.
Fast graph generative model via spectral diffusion
Tianze Luo, Zhanfeng Mo, and Sinno Jialin Pan · 2022
Later among the works it cites.
Spectre: Spectral conditioning helps to overcome the expressivity limits of one-shot graph generators
Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, and Roger Wattenhofer · 2022
Later among the works it cites.
Digress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2022
Later among the works it cites.
Efficient and degree-guided graph generation via discrete diffusion modeling
Xiaohui Chen, Jiaxing He, Xu Han, and Li-Ping Liu · 2023
Closest in time.
Improving graph generation by restricting graph bandwidth
Nathaniel Lee Diamant, Alex M Tseng, Kangway V Chuang, Tommaso Biancalani, and Gabriele Scalia · 2023
Closest in time.
Autoregressive diffusion model for graph generation, 2023
Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B. Aditya Prakash, and Chao Zhang · 2023
Closest in time.