Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/gasteigerjo/ppnp

PPNP & APPNP models from "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019)
https://github.com/gasteigerjo/ppnp

deep-learning gcn gnn graph-algorithms graph-classification graph-neural-networks machine-learning pagerank pytorch tensorflow

Last synced: 2 days ago
JSON representation

PPNP & APPNP models from "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019)

Awesome Lists containing this project

README 

        
# PPNP and APPNP










TensorFlow and PyTorch implementations of the model proposed in the paper:



**[Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://www.cs.cit.tum.de/daml/ppnp/)**   

by Johannes Gasteiger, Aleksandar Bojchevski, Stephan Günnemann   

Published at ICLR 2019.



Note that the author's name has changed from Johannes Klicpera to Johannes Gasteiger.



## Run the code

The easiest way to get started is by looking at the notebook `simple_example_tensorflow.ipynb` or `simple_example_pytorch.ipynb`. The notebook `reproduce_results.ipynb` shows how to reproduce the results from the paper.



## Requirements

The repository uses these packages:



```

numpy

scipy

tensorflow>=1.6,<2.0

pytorch>=1.5

```



You can install all requirements via `pip install -r requirements.txt`.

However, in practice you will only need either TensorFlow or PyTorch, depending on which implementation you use.

If you use the `networkx_to_sparsegraph` method for importing other datasets you will additionally need NetworkX.



## Installation

To install the package, run `python setup.py install`.



## Datasets

In the `data` folder you can find several datasets. If you want to use other (external) datasets, you can e.g. use the `networkx_to_sparsegraph` method in `ppnp.data.io` for converting NetworkX graphs to our SparseGraph format.



The Cora-ML graph was extracted by Aleksandar Bojchevski, and Stephan Günnemann. *"Deep gaussian embedding of attributed graphs: Unsupervised inductive learning via ranking."* ICLR 2018,   

while the raw data was originally published by Andrew Kachites McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore. *"Automating the construction of internet portals with machine learning."* Information Retrieval, 3(2):127–163, 2000.



The Citeseer graph was originally published by Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad.

*"Collective Classification in Network Data."* AI Magazine, 29(3):93–106, 2008.



The PubMed graph was originally published by Galileo Namata, Ben London, Lise Getoor, and Bert Huang. *"Query-driven Active Surveying for Collective Classification"*.  International Workshop on Mining and Learning with Graphs (MLG) 2012.



The Microsoft Academic graph was originally published by Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann. *"Pitfalls of Graph Neural Network Evaluation"*. Relational Representation Learning Workshop (R2L), NeurIPS 2018.



## Contact

Please contact [email protected] in case you have any questions.



## Cite

Please cite our paper if you use the model or this code in your own work:



```

@inproceedings{gasteiger_predict_2019,

	title = {Predict then Propagate: Graph Neural Networks meet Personalized PageRank},

	author = {Gasteiger, Johannes and Bojchevski, Aleksandar and G{\"u}nnemann, Stephan},

	booktitle={International Conference on Learning Representations (ICLR)},

	year = {2019}

}

```