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.

翻译：尽管图神经网络模型在节点分类任务中取得了成功，但边预测（即预测图中节点之间缺失或潜在链接的任务）对这些模型而言仍然是一个具有挑战性的问题。边预测的常见方法是首先获取两个节点的嵌入，然后使用预定义的评分函数来预测这两个节点之间是否存在边。在此，我们引入了一种名为Edge2Node的初步思路，该思路建议直接为每条边获取嵌入，而无需使用评分函数。这一思路旨在基于给定的边预测任务图G创建一个新图H，进而将G上的边预测任务简化为H上的节点分类任务。我们预计，这种引入方法能够激发对边预测任务的进一步研究。