论文标题
MECCH：基于Metapath上下文基于卷积的异质图神经网络
MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks
论文作者
论文摘要
提出了异质图神经网络（HGNN），以在具有多种类型的节点和边缘的结构数据上进行表示。为了处理HGNN变得深刻时的性能降解问题，研究人员将Metapaths结合到HGNN中，以与语义上的相关节点相关联，但图中很远。但是，现有的基于Metapath的模型会遭受信息损失或高计算成本。为了解决这些问题，我们提出了一种新型的Metapath环境基于卷积的异质图神经网络（MECCH）。 Mecch利用Metapath上下文，这是一种新型的图形结构，可促进无损节点信息聚合，同时避免任何冗余。具体而言，MECCH在功能预处理后应用三个新组件以有效地从输入图中提取全面信息：（1）Metapath上下文构建，（2）Metapath上下文编码器和（3）卷积Metapath Fusion。在五个现实世界的异质图数据集上进行节点分类和链接预测的实验表明，与具有提高的计算效率的最新基准相比，MECCH具有优异的预测准确性。
Heterogeneous graph neural networks (HGNNs) were proposed for representation learning on structural data with multiple types of nodes and edges. To deal with the performance degradation issue when HGNNs become deep, researchers combine metapaths into HGNNs to associate nodes closely related in semantics but far apart in the graph. However, existing metapath-based models suffer from either information loss or high computation costs. To address these problems, we present a novel Metapath Context Convolution-based Heterogeneous Graph Neural Network (MECCH). MECCH leverages metapath contexts, a new kind of graph structure that facilitates lossless node information aggregation while avoiding any redundancy. Specifically, MECCH applies three novel components after feature preprocessing to extract comprehensive information from the input graph efficiently: (1) metapath context construction, (2) metapath context encoder, and (3) convolutional metapath fusion. Experiments on five real-world heterogeneous graph datasets for node classification and link prediction show that MECCH achieves superior prediction accuracy compared with state-of-the-art baselines with improved computational efficiency.
