Revisiting Graph Neural Networks for Link Prediction
Graph neural networks (GNNs) have achieved great success in recent years. Three most common applications include node classification, link prediction, and graph classification...
While there is rich literature on node classification and graph classification, GNNs for link prediction is relatively less studied and less understood. Two representative classes of methods exist: GAE and SEAL. GAE (Graph Autoencoder) first uses a GNN to learn node embeddings for all nodes, and then aggregates the embeddings of the source and target nodes as their link representation. SEAL extracts a subgraph around the source and target nodes, labels the nodes