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  3. 2002.07087
  4. Cited By
Graph Deconvolutional Generation

Graph Deconvolutional Generation

14 February 2020
Daniel Flam-Shepherd
Tony C Wu
Alán Aspuru-Guzik
    BDL
ArXiv (abs)PDFHTML

Papers citing "Graph Deconvolutional Generation"

20 / 20 papers shown
GIN-Graph: A Generative Interpretation Network for Model-Level Explanation of Graph Neural Networks
GIN-Graph: A Generative Interpretation Network for Model-Level Explanation of Graph Neural Networks
Xiao Yue
Guangzhi Qu
Lige Gan
GANFAttAI4CE
268
0
0
08 Mar 2025
Auto-encoding Molecules: Graph-Matching Capabilities Matter
Auto-encoding Molecules: Graph-Matching Capabilities Matter
Magnus Cunow
Gerrit Großmann
GNN
239
0
0
01 Mar 2025
Heterogeneous Graph Generation: A Hierarchical Approach using Node
  Feature Pooling
Heterogeneous Graph Generation: A Hierarchical Approach using Node Feature Pooling
Hritaban Ghosh
Chen Changyu
Arunesh Sinha
Shamik Sural
197
0
0
15 Oct 2024
Overcoming Order in Autoregressive Graph Generation
Overcoming Order in Autoregressive Graph Generation
Edo Cohen-Karlik
Eyal Rozenberg
Daniel Freedman
190
3
0
04 Feb 2024
MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural
  Network
MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network
Akihiro Kishimoto
Hiroshi Kajino
Masataka Hirose
Junta Fuchiwaki
Indra Priyadarsini
Lisa Hamada
Hajime Shinohara
D. Nakano
Seiji Takeda
AI4CE
268
8
0
28 Sep 2023
Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep
  Learning Approaches in Single-Step Retrosynthesis
Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step RetrosynthesisJACS Au (JACS Au), 2023
Lin Yao
Wentao Guo
Zhen Wang
Shang Xiang
Wentan Liu
Guolin Ke
335
28
0
27 Sep 2023
MAGNet: Motif-Agnostic Generation of Molecules from Shapes
MAGNet: Motif-Agnostic Generation of Molecules from Shapes
Leon Hetzel
Johanna Sommer
Bastian Rieck
Fabian J. Theis
Stephan Günnemann
437
8
0
30 May 2023
The power of motifs as inductive bias for learning molecular
  distributions
The power of motifs as inductive bias for learning molecular distributions
Johanna Sommer
Leon Hetzel
David Lüdke
Fabian J. Theis
Stephan Günnemann
215
7
0
04 Apr 2023
Evaluating Graph Generative Models with Contrastively Learned Features
Evaluating Graph Generative Models with Contrastively Learned FeaturesNeural Information Processing Systems (NeurIPS), 2022
Hamed Shirzad
Kaveh Hassani
Danica J. Sutherland
239
9
0
13 Jun 2022
Gransformer: Transformer-based Graph Generation
Gransformer: Transformer-based Graph Generation
Ahmad Khajenezhad
Seyed Ali Osia
Mahmood Karimian
H. Beigy
357
2
0
25 Mar 2022
A Survey on Deep Graph Generation: Methods and Applications
A Survey on Deep Graph Generation: Methods and ApplicationsLOG IN (LOG IN), 2022
Yanqiao Zhu
Yuanqi Du
Yinkai Wang
Yichen Xu
Jieyu Zhang
Qiang Liu
Shu Wu
3DVGNN
489
75
0
13 Mar 2022
Molecule Generation for Drug Design: a Graph Learning Perspective
Molecule Generation for Drug Design: a Graph Learning PerspectiveFundamental Research (FR), 2022
Nianzu Yang
Huaijin Wu
Xiaoyong Pan
Ye Yuan
Junchi Yan
465
36
0
18 Feb 2022
Permutation Equivariant Generative Adversarial Networks for Graphs
Permutation Equivariant Generative Adversarial Networks for Graphs
Yoann Boget
Magda Gregorova
Alexandros Kalousis
GAN
181
0
0
07 Dec 2021
Keeping it Simple: Language Models can learn Complex Molecular
  Distributions
Keeping it Simple: Language Models can learn Complex Molecular DistributionsNature Communications (Nat Commun), 2021
Daniel Flam-Shepherd
Kevin Zhu
A. Aspuru‐Guzik
426
181
0
06 Dec 2021
Operator Autoencoders: Learning Physical Operations on Encoded Molecular
  Graphs
Operator Autoencoders: Learning Physical Operations on Encoded Molecular Graphs
Willis Hoke
D. Shea
S. Casey
AI4CE
233
1
0
26 May 2021
Brain Multigraph Prediction using Topology-Aware Adversarial Graph
  Neural Network
Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Network
Alaa Bessadok
Mohamed Ali Mahjoub
I. Rekik
MedImAI4CE
189
22
0
06 May 2021
Deep Graph Generators: A Survey
Deep Graph Generators: A SurveyIEEE Access (IEEE Access), 2020
Faezeh Faez
Yassaman Ommi
M. Baghshah
Hamid R. Rabiee
GNNAI4CE
294
71
0
31 Dec 2020
Molecular graph generation with Graph Neural Networks
Molecular graph generation with Graph Neural NetworksNeurocomputing (Neurocomputing), 2020
P. Bongini
Monica Bianchini
F. Scarselli
GNN
344
188
0
14 Dec 2020
Topology-Aware Generative Adversarial Network for Joint Prediction of
  Multiple Brain Graphs from a Single Brain Graph
Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain GraphInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2020
Alaa Bessadok
Mohamed Ali Mahjoub
I. Rekik
MedIm
239
13
0
23 Sep 2020
A Systematic Survey on Deep Generative Models for Graph Generation
A Systematic Survey on Deep Generative Models for Graph GenerationIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020
Xiaojie Guo
Bo Pan
MedIm
584
191
0
13 Jul 2020
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