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SHADOWCAST: Controllable Graph Generation

SHADOWCAST: Controllable Graph Generation

6 June 2020
W. Tann
E. Chang
Bryan Hooi
ArXivPDFHTML

Papers citing "SHADOWCAST: Controllable Graph Generation"

4 / 4 papers shown
Title
A Survey on Deep Graph Generation: Methods and Applications
A Survey on Deep Graph Generation: Methods and Applications
Yanqiao Zhu
Yuanqi Du
Yinkai Wang
Yichen Xu
Jieyu Zhang
Qiang Liu
Shu Wu
3DV
GNN
29
67
0
13 Mar 2022
Deep Multi-attributed Graph Translation with Node-Edge Co-evolution
Deep Multi-attributed Graph Translation with Node-Edge Co-evolution
Xiaojie Guo
Liang Zhao
Cameron Nowzari
S. Rafatirad
Houman Homayoun
Sai Manoj Pudukotai Dinakarrao
37
27
0
22 Mar 2020
Interpretable Graph Convolutional Neural Networks for Inference on Noisy
  Knowledge Graphs
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs
Daniel Neil
Joss Briody
A. Lacoste
Aaron Sim
Páidí Creed
Amir Saffari
GNN
66
34
0
01 Dec 2018
Interaction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and Physics
Peter W. Battaglia
Razvan Pascanu
Matthew Lai
Danilo Jimenez Rezende
Koray Kavukcuoglu
AI4CE
OCL
PINN
GNN
258
1,400
0
01 Dec 2016
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