Despite the success of graph neural network models in node classification, edge prediction (the task of predicting missing or potential relationships 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 E2N (Edge2Node) 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. For the ogbl-ddi and ogbl-collab datasets, our E2N method outperforms the state-of-the-art methods listed on the leaderboards. Our experiments on the ogbl-ddi dataset achieved a Hits@20 score of 98.79% on the validation set and 98.11% on the test set. On the ogbl-collab dataset, we achieved a Hits@50 score of 95.46% on the validation set and 95.15% on the test set.

本文介绍了一种称为E2N（Edge2Node）的新方法，通过在图G的基础上创建新图H，将边预测任务降低为H上的节点分类任务，从而直接获得每个边的嵌入，无需使用评分函数，该方法在任何边预测任务中都具有出色的性能和较低的计算成本。

Edge2Node: 将边预测减少为节点分类