Towards Autonomous Graph Data Analytics with Analytics-Augmented Generation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918355992051712 |
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| author | Wang, Qiange Chen, Chaoyi Gao, Jingqi Wang, Zihan Zhang, Yanfeng Yu, Ge |
| author_facet | Wang, Qiange Chen, Chaoyi Gao, Jingqi Wang, Zihan Zhang, Yanfeng Yu, Ge |
| contents | This paper argues that reliable end-to-end graph data analytics cannot be achieved by retrieval- or code-generation-centric LLM agents alone. Although large language models (LLMs) provide strong reasoning capabilities, practical graph analytics for non-expert users requires explicit analytical grounding to support intent-to-execution translation, task-aware graph construction, and reliable execution across diverse graph algorithms. We envision Analytics-Augmented Generation (AAG) as a new paradigm that treats analytical computation as a first-class concern and positions LLMs as knowledge-grounded analytical coordinators. By integrating knowledge-driven task planning, algorithm-centric LLM-analytics interaction, and task-aware graph construction, AAG enables end-to-end graph analytics pipelines that translate natural-language user intent into automated execution and interpretable results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_21604 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards Autonomous Graph Data Analytics with Analytics-Augmented Generation Wang, Qiange Chen, Chaoyi Gao, Jingqi Wang, Zihan Zhang, Yanfeng Yu, Ge Databases This paper argues that reliable end-to-end graph data analytics cannot be achieved by retrieval- or code-generation-centric LLM agents alone. Although large language models (LLMs) provide strong reasoning capabilities, practical graph analytics for non-expert users requires explicit analytical grounding to support intent-to-execution translation, task-aware graph construction, and reliable execution across diverse graph algorithms. We envision Analytics-Augmented Generation (AAG) as a new paradigm that treats analytical computation as a first-class concern and positions LLMs as knowledge-grounded analytical coordinators. By integrating knowledge-driven task planning, algorithm-centric LLM-analytics interaction, and task-aware graph construction, AAG enables end-to-end graph analytics pipelines that translate natural-language user intent into automated execution and interpretable results. |
| title | Towards Autonomous Graph Data Analytics with Analytics-Augmented Generation |
| topic | Databases |
| url | https://arxiv.org/abs/2602.21604 |