In this work, we present new ways to successfully train very deep GCNs. We borrow concepts from CNNs, mainly residual/dense connections and dilated convolutions, and adapt them to GCN architectures. Through extensive experiments, we show the positive effect of these deep GCN frameworks.
![](./misc/intro.png)
We do extensive experiments to show how different components (#Layers, #Filters, #Nearest Neighbors, Dilation, etc.) effect DeepGCNs
. We also provide ablation studies on different type of Deep GCNs (MRGCN, EdgeConv, GraphSage and GIN).
![](./misc/pipeline.png)
Further information and details please contact Guohao Li and Matthias Muller.
Please cite our paper if you find anything helpful,
@misc{li2019gcns,
title={Can GCNs Go as Deep as CNNs?},
author={Guohao Li and Matthias Müller and Ali Thabet and Bernard Ghanem},
year={2019},
eprint={1904.03751},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
MIT License