用注意力机制增强异构图中的元路径编码，提升节点分类效果。
Attention-Driven Metapath Encoding in Heterogeneous Graphs
- 引入序列注意力与直接注意力，完整保留元路径中所有中间节点信息。
- 在IMDB数据集上达到当前最优性能，优于主流基线模型。
- 适合研究异构图表示学习、知识图谱推理的科研人员使用。
异构图中的节点分类新兴技术之一是将消息聚合限制在预定义的、语义有意义的结构——元路径上。本文首次将注意力机制引入元路径整体编码过程，不丢弃中间节点。我们设计两种编码器：第一种利用序列注意力，将Magna提出的多跳消息传递算法扩展至元路径场景；第二种引入直接注意力，提取元路径中的语义关系。模型采用Han提出的元路径内与元路径间聚合机制，并结合LTS中为异构图定制的强大训练调度器，使模型逐步学习最难节点的分类。结果表明，该方法构建了一个鲁棒且通用的异构图语义结构捕捉框架。特别地，在文献[benchmark]引入的流行基准IMDB数据集上，本模型表现媲美最先进水平。
原文摘要 · Abstract (English)
One of the emerging techniques in node classification in heterogeneous graphs is to restrict message aggregation to pre-defined, semantically meaningful structures called metapaths. This work is the first attempt to incorporate attention into the process of encoding entire metapaths without dropping intermediate nodes. In particular, we construct two encoders: the first uses sequential attention to extend the multi-hop message passing algorithm designed in \citet{magna} to the metapath setting, and the second incorporates direct attention to extract semantic relations in the metapath. The model then employs the intra-metapath and inter-metapath aggregation mechanisms of \citet{han}. We furthermore use the powerful training scheduler specialized for heterogeneous graphs that was developed in \citet{lts}, ensuring the model slowly learns how to classify the most difficult nodes. The result is a resilient, general-purpose framework for capturing semantic structures in heterogeneous graphs. In particular, we demonstrate that our model is competitive with state-of-the-art models on performing node classification on the IMDB dataset, a popular benchmark introduced in \citet{benchmark}.
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