README.md

# GraphSAGE in PGL

[GraphSAGE](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf) is a general inductive framework that leverages node feature
information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, GraphSAGE learns a function that generates embeddings by sampling and aggregating features from a node’s local neighborhood. Based on PGL, we reproduce GraphSAGE algorithm and reach the same level of indicators as the paper in Reddit Dataset. Besides, this is an example of subgraph sampling and training in PGL.

### Datasets
The reddit dataset should be downloaded from the following links and placed in directory ```./data```. The details for Reddit Dataset can be found [here](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf).

- reddit.npz https://drive.google.com/open?id=19SphVl_Oe8SJ1r87Hr5a6znx3nJu1F2J
- reddit_adj.npz: https://drive.google.com/open?id=174vb0Ws7Vxk_QTUtxqTgDHSQ4El4qDHt


### Dependencies

- sklearn
- paddlepaddle>=1.4 (The speed can be faster in 1.5.)
- pgl

### How to run

To train a GraphSAGE model on Reddit Dataset, you can just run
```
 python train.py --use_cuda --epoch 10 --graphsage_type graphsage_mean --normalize --symmetry     
```

#### Hyperparameters

- epoch: Number of epochs default (10)
- use_cuda: Use gpu if assign use_cuda. 
- graphsage_type: We support 4 aggregator types including "graphsage_mean", "graphsage_maxpool", "graphsage_meanpool" and "graphsage_lstm".
- normalize: Normalize the input feature if assign normalize.
- sample_workers: The number of workers for multiprocessing subgraph sample.
- lr: Learning rate.
- symmetry: Make the edges symmetric if assign symmetry.
- batch_size: Batch size.
- samples_1: The max neighbors for the first hop neighbor sampling. (default: 25)
- samples_2: The max neighbors for the second hop neighbor sampling. (default: 10)
- hidden_size: The hidden size of the GraphSAGE models.


### Performance

We train our models for 200 epochs and report the accuracy on the test dataset.


| Aggregator | Accuracy   | Reported in paper |
| --- | --- | --- |
| Mean | 95.70% |  95.0% | 
| Meanpool | 95.60% | 94.8% |
| Maxpool | 94.95%  | 94.8% |
| LSTM | 95.13% | 95.4% |