论文标题
结构增强的图形神经网络用于链接预测
Structure Enhanced Graph Neural Networks for Link Prediction
论文作者
论文摘要
图神经网络（GNN）在各种任务中显示出令人鼓舞的结果，其中链接预测是一个重要的。 GNN模型通常遵循以节点为中心的消息传递过程，该过程将邻域信息递归地汇总到中央节点。遵循此范式，节点的特征通过边缘传递，而不必关心节点的位置以及它们扮演的角色。但是，被忽视的拓扑信息对于链接预测任务很有价值。在本文中，我们提出了结构增强的图形神经网络（SEG），以进行链接预测。 SEG引入了路径标记方法，以捕获目标节点的拓扑信息，然后将结构纳入普通的GNN模型。通过共同训练结构编码器和深度GNN模型，SEG融合了拓扑结构和节点特征，以充分利用图形信息。 OGB链接预测数据集的实验表明，SEG在所有三个公共数据集中都取得了最新的结果。
Graph Neural Networks (GNNs) have shown promising results in various tasks, among which link prediction is an important one. GNN models usually follow a node-centric message passing procedure that aggregates the neighborhood information to the central node recursively. Following this paradigm, features of nodes are passed through edges without caring about where the nodes are located and which role they played. However, the neglected topological information is shown to be valuable for link prediction tasks. In this paper, we propose Structure Enhanced Graph neural network (SEG) for link prediction. SEG introduces the path labeling method to capture surrounding topological information of target nodes and then incorporates the structure into an ordinary GNN model. By jointly training the structure encoder and deep GNN model, SEG fuses topological structures and node features to take full advantage of graph information. Experiments on the OGB link prediction datasets demonstrate that SEG achieves state-of-the-art results among all three public datasets.
