wehub-resource-sync
33 lines
925 B
Markdown
33 lines
925 B
Markdown
Predict then Propagate: Graph Neural Networks meet Personalized PageRank (APPNP)
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============
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- Paper link: [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://arxiv.org/abs/1810.05997)
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- Author's code repo: [https://github.com/klicperajo/ppnp](https://github.com/klicperajo/ppnp).
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Dependencies
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------------
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- MXNET 1.5+
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- requests
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``bash
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pip install torch requests
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``
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Code
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-----
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The folder contains an implementation of APPNP (`appnp.py`).
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Results
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-------
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Run with following (available dataset: "cora", "citeseer", "pubmed")
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```bash
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DGLBACKEND=mxnet python3 appnp.py --dataset cora --gpu 0
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```
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* cora: 0.8370 (paper: 0.850)
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* citeseer: 0.713 (paper: 0.757)
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* pubmed: 0.798 (paper: 0.797)
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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)
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