LLM+Graph@VLDB'2025 Workshop Summary
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908990613487616 |
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| author | Fang, Yixiang Khan, Arijit Wu, Tianxing Yan, Da Wang, Shu |
| author_facet | Fang, Yixiang Khan, Arijit Wu, Tianxing Yan, Da Wang, Shu |
| contents | The integration of large language models (LLMs) with graph-structured data has become a pivotal and fast evolving research frontier, drawing strong interest from both academia and industry. The 2nd LLM+Graph Workshop, co-located with the 51st International Conference on Very Large Data Bases (VLDB 2025) in London, focused on advancing algorithms and systems that bridge LLMs, graph data management, and graph machine learning for practical applications. This report highlights the key research directions, challenges, and innovative solutions presented by the workshop's speakers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02861 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LLM+Graph@VLDB'2025 Workshop Summary Fang, Yixiang Khan, Arijit Wu, Tianxing Yan, Da Wang, Shu Databases Artificial Intelligence The integration of large language models (LLMs) with graph-structured data has become a pivotal and fast evolving research frontier, drawing strong interest from both academia and industry. The 2nd LLM+Graph Workshop, co-located with the 51st International Conference on Very Large Data Bases (VLDB 2025) in London, focused on advancing algorithms and systems that bridge LLMs, graph data management, and graph machine learning for practical applications. This report highlights the key research directions, challenges, and innovative solutions presented by the workshop's speakers. |
| title | LLM+Graph@VLDB'2025 Workshop Summary |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2604.02861 |