In this tutorial, we will introduce a important link prediction. Overall speaking, the link prediction in CogDL can be divided into 3 types.
- Network embeddings based link prediction(HomoLinkPrediction). All unsupervised network embedding methods supports this task for homogenous graphs without node features.
- Knowledge graph completion(KGLinkPrediction and TripleLinkPrediction), including knowledge embedding methods(TransE, DistMult) and GNN base methods(RGCN and CompGCN).
- GNN base homogenous graph link prediction(GNNHomoLinkPrediction). Theoretically, all GNN models works.
| Models | |
|---|---|
| Network embeddings methods | DeepWalk, LINE, Node2Vec, ProNE NetMF, NetSMF, SDNE, Hope |
| Knowledge graph completion | TransE, DistMult, RotatE, RGCN, CompGCN |
| GNN methods | GCN and all the other GNN methods |
To implement a new GNN model for link prediction, just implement link_prediction_loss in the model which accepting thre parameters:
- Node features.
- Edge index.
- Labels. 0/1 for each item, indicating the edge exists in the graph or is a negative sample.
The overall implementation can be found at https://github.com/THUDM/cogdl/blob/master/cogdl/tasks/link_prediction.py