dgl/examples/pytorch/appnp

• Predict then Propagate: Graph Neural Networks meet Personalized PageRank (APPNP)
• • Dependencies
  • Code
  • Results

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

• PyTorch 0.4.1+
• 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")

python3 train.py --dataset cora --gpu 0

• cora: 0.8370 (paper: 0.850)
• citeseer: 0.715 (paper: 0.757)
• pubmed: 0.793 (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)