Heterogeneous Text-Attributed Graphs (HTAGs), where different types of entities are not only associated with texts but also connected by diverse relationships, have gained widespread popularity and application across various domains. However, current research on text-attributed graph learning predominantly focuses on homogeneous graphs, which feature a single node and edge type, thus leaving a gap in understanding how methods perform on HTAGs. One crucial reason is the lack of comprehensive HTAG datasets that offer original textual content and span multiple domains of varying sizes. To this end, we introduce a collection of challenging and diverse benchmark datasets for realistic and reproducible evaluation of machine learning models on HTAGs. Our HTAG datasets are multi-scale, span years in duration, and cover a wide range of domains, including movie, community question answering, academic, literature, and patent networks. We further conduct benchmark experiments on these datasets with various graph neural networks. All source data, dataset construction codes, processed HTAGs, data loaders, benchmark codes, and evaluation setup are publicly available at GitHub and Hugging Face.

翻译：异质文本属性图（HTAGs）作为一种包含多种实体类型且各实体不仅关联文本信息、还通过多样化关系相互连接的数据结构，已在众多领域获得广泛应用。然而，当前文本属性图学习的研究主要集中于仅包含单一节点类型和边类型的同质图，导致对各类方法在HTAGs上性能表现的认知存在空白。这一现状的重要成因在于缺乏覆盖多领域、多规模且包含原始文本内容的综合性HTAG数据集。为此，我们构建了一套具有挑战性且多样化的基准数据集集合，用于对HTAGs上的机器学习模型进行真实可复现的评估。本系列HTAG数据集具有多尺度特性，时间跨度长达数年，涵盖电影、社区问答、学术、文学及专利网络等多个领域。我们进一步使用多种图神经网络在这些数据集上开展了基准实验。所有原始数据、数据集构建代码、处理后的HTAGs、数据加载器、基准代码及评估设置均已发布于GitHub和Hugging Face平台。