ChatGraph: Chat with Your Graphs
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Abstract (arXiv)
Graph analysis is fundamental in real-world applications. Traditional approaches rely on SPARQL-like languages or clicking-and-dragging interfaces to interact with graph data. However, these methods either require users to possess high programming skills or support only a limited range of graph analysis functionalities. To address the limitations, we propose a large language model (LLM)-based framework called ChatGraph. With ChatGraph, users can interact with graphs through natural language, making it easier to use and more flexible than traditional approaches. The core of ChatGraph lies in generating chains of graph analysis APIs based on the understanding of the texts and graphs inputted in the user prompts. To achieve this, ChatGraph consists of three main modules: an API retrieval module that searches for relevant APIs, a graph-aware LLM module that enables the LLM to comprehend graphs, and an API chain-oriented finetuning module that guides the LLM in generating API chains.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 23 Jan 2024.
Builds on
6 of 17 references.
References · 6
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
- 2023The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)cited by 677
- 2023NExT-GPT: Any-to-Any Multimodal LLMcited by 610
- 2021A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Searchcited by 82
- 2016Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)cited by 44
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Citing papers per year: 2024: 1.
1 paper.