Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights
<|reference_start|>Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights: 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}.<|reference_end|>
@article{chen2024text-space,
    title={Text-space Graph Foundation Models: Comprehensive Benchmarks and New
  Insights},
    author={Zhikai Chen, Haitao Mao, Jingzhe Liu, Yu Song, Bingheng Li, Wei Jin,
  Bahare Fatemi, Anton Tsitsulin, Bryan Perozzi, Hui Liu, Jiliang Tang},
    journal={arXiv preprint arXiv:2406.10727},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10727},
    primaryClass={cs.LG}
}