/examples/mxnet/appnp/README.md

Predict then Propagate: Graph Neural Networks meet Personalized PageRank (APPNP)

• Paper link: Predict then Propagate: Graph Neural Networks meet Personalized PageRank
• Author's code repo: https://github.com/klicperajo/ppnp.

Dependencies

• MXNET 1.5+
• requests

bash pip install torch requests

Code

The folder contains an implementation of APPNP (appnp.py).

Results

Run with following (available dataset: "cora", "citeseer", "pubmed")

DGLBACKEND=mxnet python3 appnp.py --dataset cora --gpu 0

• cora: 0.8370 (paper: 0.850)
• citeseer: 0.713 (paper: 0.757)
• pubmed: 0.798 (paper: 0.797)

Experiments were done on dgl datasets (GCN settings) which are different from those used in the original implementation. (discrepancies are detailed in experimental section of the original paper)