Dynamic text-attributed graphs (DyTAGs) are prevalent in various real-world
scenarios, where each node and edge are associated with text descriptions, and
both the graph structure and text descriptions evolve over time. Despite their
broad applicability, there is a notable scarcity of benchmark datasets tailored
to DyTAGs, which hinders the potential advancement in many research fields. To
address this gap, we introduce Dynamic Text-attributed Graph Benchmark (DTGB),
a collection of large-scale, time-evolving graphs from diverse domains, with
nodes and edges enriched by dynamically changing text attributes and
categories. To facilitate the use of DTGB, we design standardized evaluation
procedures based on four real-world use cases: future link prediction,
destination node retrieval, edge classification, and textual relation
generation. These tasks require models to understand both dynamic graph
structures and natural language, highlighting the unique challenges posed by
DyTAGs. Moreover, we conduct extensive benchmark experiments on DTGB,
evaluating 7 popular dynamic graph learning algorithms and their variants of
adapting to text attributes with LLM embeddings, along with 6 powerful large
language models (LLMs). Our results show the limitations of existing models in
handling DyTAGs. Our analysis also demonstrates the utility of DTGB in
investigating the incorporation of structural and textual dynamics. The
proposed DTGB fosters research on DyTAGs and their broad applications. It
offers a comprehensive benchmark for evaluating and advancing models to handle
the interplay between dynamic graph structures and natural language. The
dataset and source code are available at this https URL

动态文本属性图（DyTAGs）广泛应用于各种真实场景，在其中每个节点和边缘都与文本描述相关联，且图结构和文本描述均随时间变化。为了填补这一空白，我们介绍了动态文本属性图基准（DTGB），这是一个从不同领域的大规模、时间演化的图集合，其中的节点和边缘由动态变化的文本属性和类别丰富。为了促进 DTGB 的使用，我们设计了基于四个真实用例的标准化评估过程：未来链接预测、目标节点检索、边缘分类和文本关系生成。这些任务要求模型理解动态图结构和自然语言，突出了 DyTAGs 所带来的独特挑战。此外，我们对 DTGB 进行了广泛的基准实验，评估了 7 种流行的动态图学习算法及其用于自适应文本属性的 LLM 嵌入的变体，以及 6 个强大的大语言模型（LLMs）。结果显示了现有模型在处理 DyTAGs 方面的局限性。我们的分析还展示了 DTGB 在研究结构和文本动态性融合方面的实用性。提出的 DTGB 促进了 DyTAGs 及其广泛应用的研究。它为评估和推进处理动态图结构和自然语言之间相互作用的模型提供了全面的基准。数据集和源代码可在此链接获取。