Implementation and experiments based on the paper Invertible Neural Network for Graph Prediction, accepted at the IEEE Journal on Selected Areas in Information Theory---Deep Learning for Inverse Problems.
Citation:
@ARTICLE{9950057,
author={Xu, Chen and Cheng, Xiuyuan and Xie, Yao},
journal={IEEE Journal on Selected Areas in Information Theory},
title={Invertible Neural Networks for Graph Prediction},
year={2022},
volume={3},
number={3},
pages={454-467},
doi={10.1109/JSAIT.2022.3221864}}
- Please see example.ipynb regarding how to use the method.
- The movie below visualizes how iGNN transports original densities
$X|Y$ of the three-moon dataset to their corresponding$H|Y$ . The top row plots the Wasserstein-2 penalty at each block, where larger values indicate more drastic amount of movement by the block.
