Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929387315658752 |
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| author | Chen, Zhikai Mao, Haitao Liu, Jingzhe Song, Yu Li, Bingheng Jin, Wei Fatemi, Bahare Tsitsulin, Anton Perozzi, Bryan Liu, Hui Tang, Jiliang |
| author_facet | Chen, Zhikai Mao, Haitao Liu, Jingzhe Song, Yu Li, Bingheng Jin, Wei Fatemi, Bahare Tsitsulin, Anton Perozzi, Bryan Liu, Hui Tang, Jiliang |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10727 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights Chen, Zhikai Mao, Haitao Liu, Jingzhe Song, Yu Li, Bingheng Jin, Wei Fatemi, Bahare Tsitsulin, Anton Perozzi, Bryan Liu, Hui Tang, Jiliang Machine Learning 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}. |
| title | Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.10727 |