Heterogeneous graph neural network has unleashed great potential on graph representation learning and shown superior performance on downstream tasks such as node classification and clustering. Existing heterogeneous graph learning networks are primarily designed to either rely on pre-defined meta-paths or use attention mechanisms for type-specific attentive message propagation on different nodes/edges, incurring many customization efforts and computational costs. To this end, we design a relation-centered Pooling and Convolution for Heterogeneous Graph learning Network, namely PC-HGN, to enable relation-specific sampling and cross-relation convolutions, from which the structural heterogeneity of the graph can be better encoded into the embedding space through the adaptive training process. We evaluate the performance of the proposed model by comparing with state-of-the-art graph learning models on three different real-world datasets, and the results show that PC-HGN consistently outperforms all the baseline and improves the performance maximumly up by 17.8%.
翻译:现有千差万别图表学习网络主要旨在依靠预设的元路径,或利用关注机制在不同节点/前沿传播特定类型专注的信息,引起许多定制努力和计算成本。为此,我们设计了一个以关联为核心的组合和演进促进异源图学习网络,即PC-HGN,以便能够进行特定关系抽样和交叉关系演进,从而通过适应性培训进程更好地将图的结构异质编码到嵌入空间。我们通过比较三种不同真实世界数据集的最新图表学习模型,评估拟议模型的性能,结果显示PC-HGN始终超越所有基线,并最大限度地提高17.8%的性能。