# graphSAGE-pytorch

**Repository Path**: buaachuanwang/graphSAGE-pytorch

## Basic Information

- **Project Name**: graphSAGE-pytorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-02-10
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## A PyTorch implementation of GraphSAGE

This package contains a PyTorch implementation of [GraphSAGE](http://snap.stanford.edu/graphsage/).

#### Authors of this code package: 
[Tianwen Jiang](https://github.com/twjiang) (tjiang2@nd.edu), 
[Tong Zhao](https://github.com/zhao-tong) (tzhao2@nd.edu),
[Daheng Wang](https://github.com/adamwang0705) (dwang8@nd.edu).




## Environment settings

- python==3.6.8
- pytorch==1.0.0




## Basic Usage

**Main Parameters:**

```
--dataSet     The input graph dataset. (default: cora)
--agg_func    The aggregate function. (default: Mean aggregater)
--epochs      Number of epochs. (default: 50)
--b_sz        Batch size. (default: 20)
--seed        Random seed. (default: 824)
--unsup_loss  The loss function for unsupervised learning. ('margin' or 'normal', default: normal)
--config      Config file. (default: ./src/experiments.conf)
--cuda        Use GPU if declared.
```

**Learning Method**

The user can specify a learning method by --learn_method, 'sup' is for supervised learning, 'unsup' is for unsupervised learning, and 'plus_unsup' is for jointly learning the loss of supervised and unsupervised method.

**Example Usage**

To run the unsupervised model on Cuda:
```
python -m src.main --epochs 50 --cuda --learn_method unsup
```