  Given the ubiquity of graph data and its applications in diverse domains,
building a Graph Foundation Model (GFM) that can work well across different
graphs and tasks with a unified backbone has recently garnered significant
interests. A major obstacle to achieving this goal stems from the fact that
graphs from different domains often exhibit diverse node features. Inspired by
multi-modal models that align different modalities with natural language, the
text has recently been adopted to provide a unified feature space for diverse
graphs. Despite the great potential of these text-space GFMs, current research
in this field is hampered by two problems. First, the absence of a
comprehensive benchmark with unified problem settings hinders a clear
understanding of the comparative effectiveness and practical value of different
text-space GFMs. Second, there is a lack of sufficient datasets to thoroughly
explore the methods' full potential and verify their effectiveness across
diverse settings. To address these issues, we conduct a comprehensive benchmark
providing novel text-space datasets and comprehensive evaluation under unified
problem settings. Empirical results provide new insights and inspire future
research directions. Our code and data are publicly available from
\url{https://github.com/CurryTang/TSGFM}.
