Towards Autonomous Graph Data Analytics with Analytics-Augmented Generation

TL;DR

This paper introduces Analytics-Augmented Generation (AAG), a new paradigm that enhances large language models with explicit analytical grounding to enable reliable, end-to-end graph data analytics for non-expert users.

Contribution

It proposes a novel framework that integrates knowledge-driven planning, algorithm-centric interaction, and task-aware graph construction to improve graph analytics with LLMs.

Findings

AAG enables automated execution of graph analytics from natural language.

It improves interpretability and reliability of graph analytics pipelines.

The framework supports diverse graph algorithms and user intents.

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.