gcn-ipdps19

KO | EN

gitlite — search

#python #hacktoberfest #llm #golang #go #machine-learning #rust #deep-learning #docker #windows #kubernetes #security #mcp #open-source

(★ 11)

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

gcn-ipdps19 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 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](https://github.com/GraphSAINT/GraphSAINT/tree/master/ipdps19_cpp) 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](https://drive.google.com/open?id=1zycmmDES39zVlbVCYs88JTJ1Wm5FbfLz). 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},}
```

gcn-ipdps19

gcn-ipdps19 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

gcn-ipdps19

gcn-ipdps19 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README