https://github.com/kimmeen/graphsage
A PyTorch implementation of the paper https://www-cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf
https://github.com/kimmeen/graphsage
Last synced: 2 months ago
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A PyTorch implementation of the paper https://www-cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf
- Host: GitHub
- URL: https://github.com/kimmeen/graphsage
- Owner: KimMeen
- License: apache-2.0
- Created: 2020-07-14T02:26:04.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-07-19T15:04:25.000Z (almost 5 years ago)
- Last Synced: 2025-01-06T10:13:17.275Z (4 months ago)
- Language: Python
- Size: 99.6 KB
- Stars: 1
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# GraphSage
A PyTorch implementation of the paper https://www-cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf
## Requirements
Pytorch >=1.1.0
DGL: 0.4.3.post2
## Results
The node classification results include two parts:
+ Full graph training on Cora citation dataset
+ Minibatch training on Reddit dataset
### Full graph training
Run with following to train a GraphSage network on the Cora dataset:
```
python train_full_cora.py
```
**Notice:** This version not performs neighbor sampling (i.e. Algorithm 1 in the paper) so we feed the model with the entire graph and corresponding feature matrix.
+ GraphSage-Mean: ~ 80.4%
+ GraphSage-GCN: ~ 83.4%
+ GraphSage-Pool: ~ 72.5%
### Minibatch training
Run with following to train a GraphSage network on the Reddit dataset:
```
python train_sampling_reddit.py
```
**Notice:** This version performs neighbor sampling in a layer-wise way (i.e. Algorithm 2 in the paper) so we feed the model with blocks (undirected bipartite graph).
+ GraphSage-Mean: ~ 96.33%
+ GraphSage-GCN: ~ 94.53%
+ GraphSage-Pool: ~ 88.35%
## To-do:
+ LSTM aggregator
+ Minibatch training: Inductive graph splitting
+ Unsupervised training