func: the target function to approximate
fit: rational function by nerual networks
install required python package
pip install -r requirements.txt
Choose on pre-processed datasets (Default is 1st below):
- America Revolution (unweighted), pre-processed: TSV file)
- crime (unweighted), pre-processed:TSV file
- language-country (weighted), pre-processed:TSV file
data switch can be configured by pass dataset index in remez_net.py
gen_data(data=1)
Then, run the main program:
python remez_net.py
GCN configuration is also provided. To perform regression task and compare fairly, neural network weights are removed:
python pygcn/train.py
MSE
RationalNet | GCN | |
---|---|---|
America | 0.0236 | 1.3641 |
crime | 0.26021 | 1.0605 |
language | 0.0329 | 0.3912 |
Codes for the paper
Zhiqian Chen, Feng Chen, Rongjie Lai, Xuchao Zhang, and Chang-Tien Lu, Rational Neural Networks for Approximating Jump Discontinuities of Graph Convolution Operator, International Conference on Data Mining(ICDM), Singpore, 2018
@article{chen2018icdmrationalnet
author = {Zhiqian Chen and
Feng Chen and
Rongjie Lai and
Xuchao Zhang and
Chang{-}Tien Lu},
title = {Rational Neural Networks for Approximating Jump Discontinuities of Graph Convolution Operator},
booktitle = {Proceedings of the The IEEE International Conference on Data Mining},
year = {2018},
}