Adaptive Propagation Graph Convolutional Network (AP-GCN)

This is the companion code for the paper: Spinelli I, Scardapane S, Uncini A, Adaptive Propagation Graph Convolutional Network, arXiv:2002.10306, 2020.

The Spectral K

We introduce the adaptive propagation graph convolutional network (AP-GCN), a variation of GCN wherein each node selects automatically the number of propagation steps performed across the graph.

Organization of the code

All the code for the models described in the paper can be found in model.py. An example of use which can be quickly extendet to the full experimental evaluation is provided in AP-GCN_demo.ipynb.

References

[1] Kipf, T.N. and Welling, M., 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.

[2] Klicpera J., Bojchevski A., Günnemann S. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. arXiv preprint arXiv:1810.05997