In this paper, a novel end-to-end neural link prediction model, named Hierarchical Attention Link Prediction Neural Network (HalpNet), is proposed. HalpNet comprehensively explores neighborhood information, which has proved important for link prediction, via the core component, i.e., hierarchical attention mechanism. The proposed hierarchical attention mechanism consists of two neural attention layers, modeling crucial structure information at node level and subgraph level, respectively. At node level, a structure-preserving attention is developed to preserve structure features of each node in the neighborhood subgraph. Based on the latent node features, at subgraph level, a structure-aggregating attention is designed to learn how important that each node in the subgraph is for the linkage of the target node pair and aggregate node features with learned attentions as a comprehensive subgraph representation. Given this expressive representation of neighborhood subgraph, HalpNet is able to predict link score of target node pair directly and effectively. We evaluate HalpNet on 8 benchmark datasets against 13 popular and state-of-the-art approaches. The experimental results demonstrate its significant superiority and wide applicability on link prediction problem.

在本文中，提出了一种新颖的端到端神经链接预测模型，称为分层注意力链接预测神经网络（HalpNet）。HalpNet 通过核心组件（即分层注意机制）全面探索邻域信息，这已被证明对链接预测很重要。所提出的分层注意机制由两个神经注意层组成，分别在节点级别和子图级别对关键结构信息进行建模。在节点级别，开发了一种结构保留注意来保留邻域子图中每个节点的结构特征。基于潜在节点特征，在子图级别，结构聚合注意力旨在了解子图中的每个节点对于目标节点对和聚合节点特征与作为综合子图表示的学习注意力的链接的重要性。鉴于邻域子图的这种表达性表示，HalpNet 能够直接有效地预测目标节点对的链接分数。我们根据 13 种流行和最先进的方法在 8 个基准数据集上评估 HalpNet。实验结果证明了其在链路预测问题上的显着优越性和广泛适用性。我们根据 13 种流行和最先进的方法在 8 个基准数据集上评估 HalpNet。实验结果证明了其在链路预测问题上的显着优越性和广泛适用性。我们根据 13 种流行和最先进的方法在 8 个基准数据集上评估 HalpNet。实验结果证明了其在链路预测问题上的显着优越性和广泛适用性。