README.md

Inductive Representation Learning on Large Graphs (GraphSAGE)
============

- Paper link: [http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf](http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf)
- Author's code repo: [https://github.com/williamleif/graphsage-simple](https://github.com/williamleif/graphsage-simple). Note that the original code is 
simple reference implementation of GraphSAGE.

Requirements
------------
- requests

``bash
pip install requests
``


Results
-------

### Full graph training

Run with following (available dataset: "cora", "citeseer", "pubmed")
```bash
python3 train_full.py --dataset cora --gpu 0    # full graph
```

* cora: ~0.8330 
* citeseer: ~0.7110
* pubmed: ~0.7830

### Minibatch training

Train w/ mini-batch sampling (on the Reddit dataset)
```bash
python3 train_sampling.py --num-epochs 30       # neighbor sampling
python3 train_sampling_multi_gpu.py --num-epochs 30    # neighbor sampling with multi GPU
python3 train_cv.py --num-epochs 30             # control variate sampling
python3 train_cv_multi_gpu.py --num-epochs 30   # control variate sampling with multi GPU
```

Accuracy:

| Model                 | Accuracy |
|:---------------------:|:--------:|
| Full Graph            | 0.9504   |
| Neighbor Sampling     | 0.9495   |
| Control Variate       | 0.9490   |