wehub-resource-syncDGL Implementation of the SEAL Paper
This DGL example implements the link prediction model proposed in the paper
Link Prediction Based on Graph Neural Networks
and REVISITING GRAPH NEURAL NETWORKS FOR LINK PREDICTION
The author's codes of implementation is in SEAL (pytorch)
and SEAL_ogb (torch_geometric)
Example implementor
This example was implemented by Smile during his intern work at the AWS Shanghai AI Lab.
The graph dataset used in this example
ogbl-collab
- NumNodes: 235868
- NumEdges: 2358104
- NumNodeFeats: 128
- NumEdgeWeights: 1
- NumValidEdges: 160084
- NumTestEdges: 146329
Dependencies
- python 3.6+
- Pytorch 1.5.0+
- dgl 0.6.0 +
- ogb
- pandas
- tqdm
- scipy
How to run example files
In the seal_dgl folder
run on cpu:
python main.py --gpu_id=-1 --subsample_ratio=0.1
run on gpu:
python main.py --gpu_id=0 --subsample_ratio=0.1
Performance
experiment on ogbl-collab
| method | valid-hits@50 | test-hits@50 |
|---|---|---|
| paper | 63.89(0.49) | 53.71(0.47) |
| ours | 63.56(0.71) | 53.61(0.78) |
Note: We only perform 5 trails in the experiment.