FakeEdge: Alleviate Dataset Shift in Link Prediction

Understand

Link prediction is a crucial problem in graph-structured data.

• Due to the recent success of graph neural networks (GNNs), a variety of GNN-based models were proposed to tackle the link prediction task.
• Specifically, GNNs leverage the message passing paradigm to obtain node representation, which relies on link connectivity.
• However, in a link prediction task, links in the training set are always present while ones in the testing set are not yet formed, resulting in a discrepancy of the connectivity pattern and bias of the learned representation.