【摘要】 在异质图数据收集中，由于隐私保护政策或版权限制，节点属性缺失现象普遍存在.针对属性不完备和属性完全缺失两种情况，提出了一种基于属性补全的异质图表示学习算法（HGAC）.对于属性不完备的节点，通过构建属性空间的邻接矩阵并执行图卷积来获取缺失的属性；将属性视为抽象节点，在元路径的引导下，对学习节点和属性进行拓扑嵌入，利用拓扑嵌入间的相似性来补全完全缺失的属性.在3个真实数据集上进行实验，结果表明，该算法有效提升了下游任务的性能，并具有较强的泛化能力.更多还原

【Abstract】 In the process of collecting heterogeneous graph data, node attributes are often missing due to privacy protection policies or copyright constraints. Regarding both incomplete attributes and completely missing attributes, a heterogeneous graph representation learning algorithm based on attribute completion(HGAC) was proposed. For nodes with incomplete attributes, the missing attributes were obtained by constructing an adjacency matrix in the attribute space and performing graph convolution. Subsequently, the attributes were regarded as abstract nodes, and under the guidance of meta-paths, the topological embeddings of both nodes and attributes were learned. The similarity among the topological embeddings were then used to complete completely missing attributes. Experiments conducted on three real datasets demonstrate that the proposed algorithm effectively enhances the performance of downstream tasks and possesses strong generalization capability.更多还原