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Simplifying-Graph-Convolutional-Networks 简化的图神经网络 (SGC)

A reference code for paper : Simplifying Graph Convolutional Networks ( SGC for short ) based Keras and tensorflow.

Usage

Before to execute this algorithm, it is necessary to install these required packages shown in the file named ' requirements.txt '. The default dataset is Cora Network and the detail description can be found in the file data. Just execute the following command from the project home directory :

$ python SGC.py

Our implementation can match the benchmark described in the original paper and the GPU will be used if available. Note: the default splits of the training set in my implementation are different from that in original papar, which you can find in the authors' code.

References

For a more detail explanation of the SGC, have a look at the relative references about the SGC and vanilla GCN:

  1. Wu F, Zhang T, Holanda de Souza A, et al. Simplifying graph convolutional networks[J]. ICML 2019.

  2. https://github.com/Tiiiger/SGC

  3. Thomas N. Kipf, Max Welling, Semi-Supervised Classification with Graph Convolutional Networks. ICLR 2017.

  4. https://github.com/zhouchunpong/GCN_Keras