图神经网络作为一种新的深度学习模型, 被广泛运用在图数据中, 并极大地推动了推荐系统、社交网络、知识图谱等应用的发展. 现有的异构图神经网络通常事先定义了多条元路径来学习异构图中的复合关系. 然而, 这些模型通常在特征聚合步骤中只考虑单条元路径, 导致模型只关注了元路径的局部结构, 忽略了元路径之间的全局相关性; 还有一些模型则是忽略掉了元路径的中间节点和边信息, 导致模型无法学习到元路径内部的语义信息. 针对以上问题, 本文提出一种基于元路径的图Transformer神经网络(MaGTNN). 该模型首先将异构图采样为基于元路径的多关系子图, 利用提出的位置编码和边编码的方法来获取元路径中的语义信息. 随后使用改进的图Transformer层计算出目标节点与其元邻居的相似度, 并利用该相似度来聚合其所有的元邻居信息. 在3个公开数据集的节点分类和节点聚类任务中, MaGTNN均高于最新的基准模型.
As new deep learning models, graph neural networks are widely used in graph data and promote various applications, such as recommendation systems, social networks, and knowledge graphs. Most existing heterogeneous graph neural models usually predefine multiple metapaths to capture composite relationships in heterogeneous graphs. However, some models usually consider one metapath during the feature aggregation, leading to models only learning neighbor structure but ignoring the global correlation of multiple matapaths. Others omit intermediate nodes and edges along the metapath, which means models cannot learn the semantic information in each metapath. To address those limitations, this study proposes a new model named metapath-based graph Transformer neural network (MaGTNN). Specifically, MaGTNN first samples heterogeneous graph as metapath-based multi-relation graph and then uses the proposed position encoder and edge encoder to capture the semantic information in a metapath. Subsequently, all the matapath-based neighbor information is aggregated to the target node through their similarity, which is calculated by the improved graph Transformer layer. Extensive experiments on three real-world heterogeneous graph datasets for node classification and node clustering show that MaGTNN achieves more accurate prediction results than state-of-the-art baselines.