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
https://github.com/CurryTang/TSGFM.

建立一个可以在不同图和任务中很好工作的图基础模型（GFM），通过统一的骨干结构近期引起了广泛关注。尽管文本空间的 GFM 存在巨大潜力，但目前的研究面临两个问题：缺乏统一问题设置的综合基准，以及缺乏足够的数据集来全面探索方法的潜力。为了解决这些问题，我们进行了综合基准的研究，并提供了新的文本空间数据集和全面的评估。实证结果提供了新的见解，并启发了未来的研究方向。