Accurate, Efficient and Scalable Graph Embedding

Open source code for the GCN training algorithm in the IEEE/IPDPS'19 paper.

Right now, for ease of extending the framework, we have integrated our sampling and feature propagation algorithm into Tensorflow (where the subgraph feature propagation as a self-defined operation).

Important Note: Please go to this repo for the C++ implementation.

Contact

Hanqing Zeng: zengh@usc.edu, Hongkuan Zhou: hongkuaz@usc.edu

Dependencies

• python >= 3.6.8
• tensorflow >=1.12.0
• numpy >= 1.14.3
• scipy >= 1.1.0
• scikit-learn >= 0.19.1
• openmp >= 4.0
• mkl >= 2018.0.2

Dataset

There are three datasets (PPI, Reddit and Yelp) available via this Google Drive link. Rename the folder to data at the root directory. The directory structure should be as below:

GCN-IPDPS19/
│   README.md
│   run_gs_subgraph.sh
│   ... 
│
└───gcn_ipdps19/
│   │   models.py
│   │   train.py
│   │   ...
│   
└───data/
│   └───ppi/
│   │   │    adj_train.npz
│   │   │    adj_full.npz
│   │   │    ...
│   │   
│   └───reddit/
│   │   │    ...
│   │
│   └───...
│

Run Training

There are two options to run the code on CPU with mkl. You can either use the --mkl flag to directly call C++ mkl libraries or use the mkl-built tensorflow.

To run the code on CPU with mkl-built tensorflow:

./run_training.sh <dataset_name> <path to train_config yml>

To run the code on CPU with python-build tensorflow:

./run_training.sh <dataset_name> <path to train_config yml> --mkl

To run the code on GPU:

./run_training.sh <dataset_name> <path to train_config yml> --gpu <GPU number>

For example --gpu 0 will run on the fisrt GPU.

Citation

@INPROCEEDINGS{8820993, 
author={H. {Zeng} and H. {Zhou} and A. {Srivastava} and R. {Kannan} and V. {Prasanna}}, 
booktitle={2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)}, 
title={Accurate, Efficient and Scalable Graph Embedding}, 
year={2019}, 
volume={}, 
number={}, 
pages={462-471}, 
keywords={Graph embedding;Graph convolutional networks;Subgraph sampling}, 
doi={10.1109/IPDPS.2019.00056}, 
ISSN={}, 
month={May},}