Predict then Propagate: Graph Neural Networks meet Personalized PageRank (APPNP) ============ - Paper link: [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://arxiv.org/abs/1810.05997) - Author's code repo: [https://github.com/klicperajo/ppnp](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") ```bash python 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)