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. In this paper, we introduce a new approach called Edge2Node (E2N) which directly obtains an embedding for each edge, without the need for a scoring function. To do this, we create a new graph H based on the graph G given for the edge prediction task, and then reduce the edge prediction task on G to a node classification task on H. Our E2N method can be easily applied to any edge prediction task with superior performance and lower computational costs. Our E2N method beats the best-known methods on the leaderboards for ogbl-ppa, ogbl-collab, and ogbl-ddi datasets by 25.89%, 24.19%, and 0.34% improvements, respectively.

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（预测图中节点之间缺失或潜在连接的任务）对这些模型来说仍然是一个具有挑战性的问题。边预测的常用方法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。在本文中，我们提出了一种名为Edge2Node（E2N）的新方法，该方法可直接获取每条边的嵌入，无需评分函数。为此，我们基于给定边预测任务的图G构建一个新图H，然后将图G上的边预测任务简化为图H上的节点分类任务。我们的E2N方法可以轻松应用于任何边预测任务，具有更优的性能和更低的计算成本。在ogbl-ppa、ogbl-collab和ogbl-ddi数据集的排行榜上，我们的E2N方法分别以25.89%、24.19%和0.34%的提升幅度超越了已知最佳方法。