论文标题
仅具有属性信息的节点的归纳链接预测
Inductive Link Prediction for Nodes Having Only Attribute Information
论文作者
论文摘要
预测两个节点之间的链接是图数据分析的基本问题。在属性图中，可以将结构和属性信息用于链接预测。大多数现有的研究都集中在两个节点已经在图中的偏置链接预测上。但是，许多现实世界应用需要仅具有属性信息的新节点的归纳预测。这更具挑战性，因为新节点没有结构信息，并且在模型培训期间无法看到。为了解决这个问题，我们提出了一个称为Deal的模型，该模型由三个组成部分组成：两个节点嵌入编码器和一个对齐机制。这两个编码器旨在输出面向属性的节点嵌入和面向结构的节点嵌入，并且对齐机制使两种类型的嵌入方式对齐，以在属性和链接之间构建连接。我们的模型交易用途广泛，因为它既适用于归纳和托管链接预测。在几个基准数据集上进行的广泛实验表明，我们提出的模型显着胜过现有的归纳链路预测方法，并且在转导链路预测上的最新方法也优于最新方法。
Predicting the link between two nodes is a fundamental problem for graph data analytics. In attributed graphs, both the structure and attribute information can be utilized for link prediction. Most existing studies focus on transductive link prediction where both nodes are already in the graph. However, many real-world applications require inductive prediction for new nodes having only attribute information. It is more challenging since the new nodes do not have structure information and cannot be seen during the model training. To solve this problem, we propose a model called DEAL, which consists of three components: two node embedding encoders and one alignment mechanism. The two encoders aim to output the attribute-oriented node embedding and the structure-oriented node embedding, and the alignment mechanism aligns the two types of embeddings to build the connections between the attributes and links. Our model DEAL is versatile in the sense that it works for both inductive and transductive link prediction. Extensive experiments on several benchmark datasets show that our proposed model significantly outperforms existing inductive link prediction methods, and also outperforms the state-of-the-art methods on transductive link prediction.
