Paper
Towards Autonomous Graph Data Analytics with Analytics-Augmented Generation
Authors
Qiange Wang, Chaoyi Chen, Jingqi Gao, Zihan Wang, Yanfeng Zhang, Ge Yu
Abstract
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.21604v1</id>\n <title>Towards Autonomous Graph Data Analytics with Analytics-Augmented Generation</title>\n <updated>2026-02-25T06:09:53Z</updated>\n <link href='https://arxiv.org/abs/2602.21604v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.21604v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.DB'/>\n <published>2026-02-25T06:09:53Z</published>\n <arxiv:comment>8 pages, 7 figures</arxiv:comment>\n <arxiv:primary_category term='cs.DB'/>\n <author>\n <name>Qiange Wang</name>\n </author>\n <author>\n <name>Chaoyi Chen</name>\n </author>\n <author>\n <name>Jingqi Gao</name>\n </author>\n <author>\n <name>Zihan Wang</name>\n </author>\n <author>\n <name>Yanfeng Zhang</name>\n </author>\n <author>\n <name>Ge Yu</name>\n </author>\n </entry>"
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