Fetching the paper…
Reading the bibliography…
Deep generative models have achieved great success in areas such as image, speech, and natural language processing in the past few years.
On the evolution of random graphs
Paul Erdős and Alfréd Rényi · 1960
Earlier work this paper cites.
Graph grammars with neighbourhood-controlled embedding
Dirk Janssens and Grzegorz Rozenberg · 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.
A new data structure for cumulative frequency tables
Peter M Fenwick · 1994
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Hyperedge replacement graph grammars
Frank Drewes, H-J Kreowski, and Annegret Habel · 1997
Earlier work this paper cites.
Collective dynamics of ‘small-world’networks
Duncan J Watts and Steven H Strogatz · 1998
Earlier work this paper cites.
Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart J Russell, et al · 2000
Earlier work this paper cites.
Statistical mechanics of complex networks
Réka Albert and Albert-László Barabási · 2002
Earlier work this paper cites.
gspan: Graph-based substructure pattern mining
Xifeng Yan and Jiawei Han · 2002
Earlier work this paper cites.
R-mat: A recursive model for graph mining
Deepayan Chakrabarti, Yiping Zhan, and Christos Faloutsos · 2004
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
Earlier work this paper cites.
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl and Ansgar Schuffenhauer · 2009
Earlier work this paper cites.
Kronecker graphs: an approach to modeling networks
Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos, and Zoubin Ghahramani · 2010
Earlier work this paper cites.
Kernel topic models
Philipp Hennig, David Stern, Ralf Herbrich, and Thore Graepel · 2012
Earlier work this paper cites.
Quantifying the chemical beauty of drugs
G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins · 2012
Earlier work this paper cites.
Reasoning with neural tensor networks for knowledge base completion
Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Learning phrase representations using rnn encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
Earlier work this paper cites.
Order matters: Sequence to sequence for sets
Oriol Vinyals, Samy Bengio, and Manjunath Kudlur · 2015
Earlier work this paper cites.
Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
Earlier work this paper cites.
A transition-based algorithm for amr parsing
Chuan Wang, Nianwen Xue, and Sameer Pradhan · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Earlier work this paper cites.
Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
Earlier work this paper cites.
Attribute2image: Conditional image generation from visual attributes
Xinchen Yan, Jimei Yang, Kihyuk Sohn, and Honglak Lee · 2016
Earlier work this paper cites.
Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
Earlier work this paper cites.
Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
Earlier work this paper cites.
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Adversarial feature matching for text generation
Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, and Lawrence Carin · 2017
Earlier work this paper cites.
Sequence-to-sequence voice conversion with similarity metric learned using generative adversarial networks
Takuhiro Kaneko, Hirokazu Kameoka, Kaoru Hiramatsu, and Kunio Kashino · 2017
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.
Dynamic edge-conditioned filters in convolutional neural networks on graphs
Martin Simonovsky and Nikos Komodakis · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Earlier work this paper cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Earlier work this paper cites.
Grammar variational autoencoder
MJ Kusner, B Paige, and JM Hernández-Lobato · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Earlier work this paper cites.
Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2017
Earlier work this paper cites.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
Earlier work this paper cites.
Least squares generative adversarial networks
Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley · 2017
Earlier work this paper cites.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
Earlier work this paper cites.
Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
Earlier work this paper cites.
Molecular de-novo design through deep reinforcement learning
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen · 2017
Earlier work this paper cites.
Deep biaffine attention for neural dependency parsing
Timothy Dozat and Christopher D. Manning · 2017
Earlier work this paper cites.
Learning graphical state transitions
Daniel D. Johnson · 2017
Earlier work this paper cites.
Scene graph generation from objects, phrases and region captions
Yikang Li, Wanli Ouyang, Bolei Zhou, Kun Wang, and Xiaogang Wang · 2017
Earlier work this paper cites.
Scene graph generation by iterative message passing
Danfei Xu, Yuke Zhu, Christopher B Choy, and Li Fei-Fei · 2017
Earlier work this paper cites.
Pixels to graphs by associative embedding
Alejandro Newell and Jia Deng · 2017
Cited alongside, same era.
Latent intention dialogue models
Tsung-Hsien Wen, Yishu Miao, Phil Blunsom, and Steve Young · 2017
Cited alongside, same era.
Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing · 2017
Cited alongside, same era.
Multi-objective de novo drug design with conditional graph generative model
Yibo Li, Liangren Zhang, and Zhenming Liu · 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.
Learning deep generative models of graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia · 2018
Graph matching networks for learning the similarity of graph structured objects
Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, and Pushmeet Kohli · 2019
Later among the works it cites.
Adversarial attacks on graph neural networks via meta learning
Daniel Zügner and Stephan Günnemann · 2019
Later among the works it cites.
Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks
Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian Reid, Hamid Rezatofighi, and Silvio Savarese · 2019
Later among the works it cites.
Riemannian normalizing flow on variational wasserstein autoencoder for text modeling
Prince Zizhuang Wang and William Yang Wang · 2019
Later among the works it cites.
Attention models in graphs: A survey
John Boaz Lee, Ryan A Rossi, Sungchul Kim, Nesreen K Ahmed, and Eunyee Koh · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander Gaunt · 2018
Cited alongside, same era.
Defactor: Differentiable edge factorization-based probabilistic graph generation
Rim Assouel, Mohamed Ahmed, Marwin H Segler, Amir Saffari, and Yoshua Bengio · 2018
Cited alongside, same era.
Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec · 2018
Cited alongside, same era.
Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
Cited alongside, same era.
Constrained generation of semantically valid graphs via regularizing variational autoencoders
Tengfei Ma, Jie Chen, and Cao Xiao · 2018
Cited alongside, same era.
Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
Cited alongside, same era.
Guixiang Ma, Nesreen K Ahmed, Theodore L Willke, and Philip S Yu · 2019
Later among the works it cites.
Auto-regressive graph generation modeling with improved evaluation methods
Chia-Cheng Liu, Harris Chan, Kevin Luk, and AI Borealis · 2019
Later among the works it cites.
Graph to graph: a topology aware approach for graph structures learning and generation
Mingming Sun and Ping Li · 2019
Later among the works it cites.
Disentangling interpretable generative parameters of random and real-world graphs
Niklas Stoehr, Marc Brockschmidt, Jan Stuehmer, and Emine Yilmaz · 2019
Later among the works it cites.
Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction
Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A Hunter, Costas Bekas, and Alpha A Lee · 2019
Later among the works it cites.
A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Jan H Jensen · 2019
Later among the works it cites.
Stggan: Spatial-temporal graph generation
Liming Zhang · 2019
Later among the works it cites.
Conditional labeled graph generation with gans
Shuangfei Fan and Bert Huang · 2019
Later among the works it cites.
Amr parsing as sequence-to-graph transduction
Sheng Zhang, Xutai Ma, Kevin Duh, and Benjamin Van Durme · 2019
Later among the works it cites.
Scene graph generation with external knowledge and image reconstruction
Jiuxiang Gu, Handong Zhao, Zhe Lin, Sheng Li, Jianfei Cai, and Mingyang Ling · 2019
Later among the works it cites.
Attentive relational networks for mapping images to scene graphs
Mengshi Qi, Weijian Li, Zhengyuan Yang, Yunhong Wang, and Jiebo Luo · 2019
Later among the works it cites.
Knowledge-embedded routing network for scene graph generation
Tianshui Chen, Weihao Yu, Riquan Chen, and Liang Lin · 2019
Later among the works it cites.
Rpgan: Gans interpretability via random routing
Andrey Voynov and Artem Babenko · 2019
Later among the works it cites.
Ctrl: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R Varshney, Caiming Xiong, and Richard Socher · 2019
Later among the works it cites.
Edge-based sequential graph generation with recurrent neural networks
Davide Bacciu, Alessio Micheli, and Marco Podda · 2020
Closest in time.
Graphgen: A scalable approach to domain-agnostic labeled graph generation
Nikhil Goyal, Harsh Vardhan Jain, and Sayan Ranu · 2020
Closest in time.
Attention-based graph evolution
Shuangfei Fan and Bert Huang · 2020
Closest in time.
Scalable deep generative modeling for sparse graphs
Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, and Dale Schuurmans · 2020
Closest in time.
Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2020
Closest in time.
Sohil Atul Shah and Vladlen Koltun · 2020
Closest in time.
Hierarchical generation of molecular graphs using structural motifs
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
Closest in time.
Scaffold-based molecular design with a graph generative model
Jaechang Lim, Sang-Yeon Hwang, Seokhyun Moon, Seungsu Kim, and Woo Youn Kim · 2020
Closest in time.
Network-principled deep generative models for designing drug combinations as graph sets
Mostafa Karimi, Arman Hasanzadeh, and Yang Shen · 2020
Closest in time.
Deepgraphmolgen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach
Yash Khemchandani, Stephen O’Hagan, Soumitra Samanta, Neil Swainston, Timothy J Roberts, Danushka Bollegala, and Douglas B Kell · 2020
Closest in time.
Graphopt: Learning optimization models of graph formation
Rakshit Trivedi, Jiachen Yang, and Hongyuan Zha · 2020
Closest in time.
Reinforced molecular optimization with neighborhood-controlled grammars
Chencheng Xu, Qiao Liu, Minlie Huang, and Tao Jiang · 2020
Closest in time.
Guiding deep molecular optimization with genetic exploration
Sungsoo Ahn, Junsu Kim, Hankook Lee, and Jinwoo Shin · 2020
Closest in time.
Graph deconvolutional generation
Daniel Flam-Shepherd, Tony Wu, and Alan Aspuru-Guzik · 2020
Closest in time.
Node-edge co-disentangled representation learning for attributed graph generation
Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, and Yanfang Ye · 2020
Closest in time.
Dirichlet graph variational autoencoder
Jia Li, Jianwei Yu, Jiajin Li, Honglei Zhang, Kangfei Zhao, Yu Rong, Hong Cheng, and Junzhou Huang · 2020
Closest in time.
Multi-motifgan (mmgan): Motif-targeted graph generation and prediction
Anuththari Gamage, Eli Chien, Jianhao Peng, and Olgica Milenkovic · 2020
Closest in time.
Shadowcast: Controlling network properties to explain graph generation
Wesley Joon-Wie Tann, Ee-Chien Chang, and Bryan Hooi · 2020
Closest in time.
Learn to generate time series conditioned graphs with generative adversarial nets
Shanchao Yang, Jing Liu, Kai Wu, and Mingming Li · 2020
Closest in time.
Mol-cyclegan: a generative model for molecular optimization
Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Tomasz Danel, and Michał Warchoł · 2020
Closest in time.
Unsupervised joint k k -node graph representations with compositional energy-based models
Leonardo Cotta, Carlos HC Teixeira, Ananthram Swami, and Bruno Ribeiro · 2020
Closest in time.
Gcn meets gpu: Decoupling “when to sample” from “how to sample”
Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi, Anand Sivasubramaniam, and Mahmut Kandemir · 2020
Closest in time.
Deep graph matching consensus
Matthias Fey, Jan E Lenssen, Christopher Morris, Jonathan Masci, and Nils M Kriege · 2020
Closest in time.
Deep learning on graphs: A survey
Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2020
Closest in time.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
Closest in time.
A comprehensive survey on geometric deep learning
Wenming Cao, Zhiyue Yan, Zhiquan He, and Zhihai He · 2020
Closest in time.
A gentle introduction to deep learning for graphs
Davide Bacciu, Federico Errica, Alessio Micheli, and Marco Podda · 2020
Closest in time.
Learning for graph matching and related combinatorial optimization problems
Junchi Yan, Shuang Yang, and Edwin R Hancock · 2020
Closest in time.
Rein: Flexible mesh generation from point clouds
Rangel Daroya, Rowel Atienza, and Rhandley Cajote · 2020
Closest in time.
Deepnc: Deep generative network completion
Cong Tran, Won-Yong Shin, Andreas Spitz, and Michael Gertz · 2020
Closest in time.
Julian Stier and Michael Granitzer · 2020
Closest in time.
Deep generative probabilistic graph neural networks for scene graph generation
Mahmoud Khademi and Oliver Schulte · 2020
Closest in time.
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
Closest in time.
Explainable anatomical shape analysis through deep hierarchical generative models
Carlo Biffi, Juan J Cerrolaza, Giacomo Tarroni, Wenjia Bai, Antonio De Marvao, Ozan Oktay, Christian Ledig, Loic Le Folgoc, Konstantinos Kamnitsas, Georgia Doumou, et al · 2020
Closest in time.
Dispersed exponential family mixture vaes for interpretable text generation
Wenxian Shi, Hao Zhou, Ning Miao, and Lei Li · 2020
Closest in time.