MetaFill：异构信息网络元路径生成的文本填充
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks
摘要：
异构信息网络（HIN）是研究包含多种边类型和节点类型的复杂网络的基础。元路径是嵌入嵌入异构网络的核心技术，它是一系列节点类型和边类型的集合。由于手动管理元路径非常耗时，因此迫切需要开发自动化元路径生成方法。现有的元路径生成方法不能充分利用HIN中丰富的文本信息，如节点名称和边类型名称。为了解决这个问题，我们提出MetaFill，一个基于文本填充的元路径生成方法。MetaFill的核心思想是将元路径识别问题转化为一个词序列填充问题，并通过预训练的语言模型（PLM）来实现。我们观察到MetaFill相对于现有元路径生成方法和图嵌入方法的优越性能，这些方法在两个真实世界的HIN数据集上的链接预测和节点分类中都不利用元路径。我们进一步演示了MetaFill如何在zero-shot设置中准确地对边缘进行分类，其中现有方法无法生成任何元路径。MetaFill利用PLM生成图嵌入的元路径，为图分析中的语言模型应用开辟了新的途径。
英文摘要：
Heterogeneous Information Network (HIN) is essential to study complicated networks containing multiple edge types and node types. Meta-path, a sequence of node types and edge types, is the core technique to embed HINs. Since manually curating meta-paths is time-consuming, there is a pressing need to develop automated meta-path generation approaches. Existing meta-path generation approaches cannot fully exploit the rich textual information in HINs, such as node names and edge type names. To address this problem, we propose MetaFill, a text-infilling-based approach for meta-path generation. The key idea of MetaFill is to formulate meta-path identification problem as a word sequence infilling problem, which can be advanced by Pretrained Language Models (PLMs). We observed the superior performance of MetaFill against existing meta-path generation methods and graph embedding methods that do not leverage meta-paths in both link prediction and node classification on two real-world HIN datasets. We further demonstrated how MetaFill can accurately classify edges in the zero-shot setting, where existing approaches cannot generate any meta-paths. MetaFill exploits PLMs to generate meta-paths for graph embedding, opening up new avenues for language model applications in graph analysis.
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