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DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
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[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/dropedge-towards-deep-graph-convolutional/node-classification-on-cora-full-supervised)](https://paperswithcode.com/sota/node-classification-on-cora-full-supervised?p=dropedge-towards-deep-graph-convolutional)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/dropedge-towards-deep-graph-convolutional/node-classification-on-citeseer-full)](https://paperswithcode.com/sota/node-classification-on-citeseer-full?p=dropedge-towards-deep-graph-convolutional)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/dropedge-towards-deep-graph-convolutional/node-classification-on-pubmed-full-supervised)](https://paperswithcode.com/sota/node-classification-on-pubmed-full-supervised?p=dropedge-towards-deep-graph-convolutional)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/dropedge-towards-deep-graph-convolutional/node-classification-on-reddit)](https://paperswithcode.com/sota/node-classification-on-reddit?p=dropedge-towards-deep-graph-convolutional)
This is a Pytorch implementation of paper: DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
......@@ -24,7 +29,7 @@ The data format is same as [GCN](https://github.com/tkipf/gcn). We provide three
## Benchmark Results
For the details of backbones in Tables, please refer to the Appendix B.2 in papers. All results are obtained on GPU (CUDA Version 9.0.176).
For the details of backbones in Tables, please refer to the Appendix B.2 in the paper. All results are obtained on GPU (CUDA Version 9.0.176).
### Full-supervised Setting Results
The following table demonstrates the testing accuracy (%) comparisons on different backbones and layers w and w/o DropEdge.
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