DGL 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+
• 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.