The goal of network representation learning is to extract deep-level abstraction from data features that can also be viewed as a process of transforming the high-dimensional data to low-dimensional features. Learning the mapping functions between two vector spaces is an essential problem. In this paper, we propose a new similarity index based on traditional machine learning, which integrates the concepts of common neighbor, local path, and preferential attachment. Furthermore, for applying the link prediction methods to the field of node classification, we have innovatively established an architecture named multitask graph autoencoder. Specifically, in the context of structural deep network embedding, the architecture designs a framework of high-order loss function by calculating the node similarity from multiple angles so that the model can make up for the deficiency of the second-order loss function. Through the parameter fine-tuning, the high-order loss function is introduced into the optimized autoencoder. Proved by the effective experiments, the framework is generally applicable to the majority of classical similarity indexes.

中文翻译：

---

基于多任务图自动编码器的链路预测和节点分类

网络表示学习的目标是从数据特征中提取深度抽象，也可以将其视为将高维数据转换为低维特征的过程。学习两个向量空间之间的映射函数是一个基本问题。在本文中，我们提出了一种基于传统机器学习的新的相似性指标，该指标融合了公共邻居，局部路径和优先依附的概念。此外，为了将链接预测方法应用于节点分类领域，我们创新地建立了一种称为多任务图自动编码器的体系结构。具体来说，在结构化深层网络嵌入的背景下，该架构通过从多个角度计算节点相似度来设计高阶损失函数框架，从而弥补了二阶损失函数的不足。通过参数微调，高阶损耗函数被引入到优化的自动编码器中。经过有效的实验证明，该框架通常适用于大多数经典相似性指标。