  The task of inferring the missing links in a graph based on its current
structure is referred to as link prediction. Link prediction methods that are
based on pairwise node similarity are well-established approaches in the
literature. They show good prediction performance in many real-world graphs
though they are heuristics and lack of universal applicability. On the other
hand, the success of neural networks for classification tasks in various
domains leads researchers to study them in graphs. When a neural network can
operate directly on the graph, then it is termed as the graph neural network
(GNN). GNN is able to learn hidden features from graphs which can be used for
link prediction task in graphs. Link predictions based on GNNs have gained much
attention of researchers due to their convincing high performance in many
real-world graphs. This appraisal paper studies some similarity and GNN-based
link prediction approaches in the domain of homogeneous graphs that consists of
a single type of (attributed) nodes and single type of pairwise links. We
evaluate the studied approaches against several benchmark graphs with different
properties from various domains.
