  Text-Attributed Graphs (TAGs) augment graph structures with natural language
descriptions, facilitating detailed depictions of data and their
interconnections across various real-world settings. However, existing TAG
datasets predominantly feature textual information only at the nodes, with
edges typically represented by mere binary or categorical attributes. This lack
of rich textual edge annotations significantly limits the exploration of
contextual relationships between entities, hindering deeper insights into
graph-structured data. To address this gap, we introduce Textual-Edge Graphs
Datasets and Benchmark (TEG-DB), a comprehensive and diverse collection of
benchmark textual-edge datasets featuring rich textual descriptions on nodes
and edges. The TEG-DB datasets are large-scale and encompass a wide range of
domains, from citation networks to social networks. In addition, we conduct
extensive benchmark experiments on TEG-DB to assess the extent to which current
techniques, including pre-trained language models, graph neural networks, and
their combinations, can utilize textual node and edge information. Our goal is
to elicit advancements in textual-edge graph research, specifically in
developing methodologies that exploit rich textual node and edge descriptions
to enhance graph analysis and provide deeper insights into complex real-world
networks. The entire TEG-DB project is publicly accessible as an open-source
repository on Github, accessible at
https://github.com/Zhuofeng-Li/TEG-Benchmark.
