  In this paper, we benchmark several existing graph neural network (GNN)
models on different datasets for link predictions. In particular, the graph
convolutional network (GCN), GraphSAGE, graph attention network (GAT) as well
as variational graph auto-encoder (VGAE) are implemented dedicated to link
prediction tasks, in-depth analysis are performed, and results from several
different papers are replicated, also a more fair and systematic comparison are
provided. Our experiments show these GNN architectures perform similarly on
various benchmarks for link prediction tasks.
