  Despite the success of graph neural network models in node classification,
edge prediction (the task of predicting missing or potential links between
nodes in a graph) remains a challenging problem for these models. A common
approach for edge prediction is to first obtain the embeddings of two nodes,
and then a predefined scoring function is used to predict the existence of an
edge between the two nodes. Here, we introduce a preliminary idea called
Edge2Node which suggests to directly obtain an embedding for each edge, without
the need for a scoring function. This idea wants to create a new graph H based
on the graph G given for the edge prediction task, and then suggests reducing
the edge prediction task on G to a node classification task on H. We anticipate
that this introductory method could stimulate further investigations for edge
prediction task.
