GraphChain: Large Language Models for Large-scale Graph Analysis via Tool Chaining

2025-11-01

AI Summary

Large language models (LLMs) face dual bottlenecks in large-scale graph analysis: limited context length and topology-agnostic reasoning. To address these, we propose GraphAdapt—a dynamic, toolchain-driven adaptive graph analysis framework. Methodologically, it introduces: (1) a progressive graph distillation mechanism that compresses graphs efficiently while preserving task-critical structural information; (2) a structure-aware test-time adaptation strategy, leveraging spectral graph analysis to dynamically orchestrate lightweight adapters and tool invocation sequences; and (3) reinforcement learning–based optimization of tool coordination. Extensive experiments on multiple large-scale graph benchmarks demonstrate that GraphAdapt significantly improves LLMs’ accuracy and scalability on subgraph matching and anomaly detection tasks—achieving an average 12.7% gain over state-of-the-art methods—while reducing inference overhead by 38%.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Natural Language Processing: (Large) Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Large Language Models (LLMs) face significant limitations when applied to large-scale graphs, struggling with context constraints and inflexible reasoning. We present GraphChain, a framework that enables LLMs to analyze complex graphs through dynamic sequences of specialized tools, mimicking human exploratory intelligence. Our approach introduces two key innovations: (1) Progressive Graph Distillation, a reinforcement learning mechanism that generates optimized tool sequences balancing task relevance with information compression, and (2) Structure-aware Test-Time Adaptation, which efficiently tailors tool selection strategies to diverse graph topologies using spectral properties and lightweight adapters without costly retraining. Experiments show GraphChain significantly outperforms prior methods, enabling scalable and adaptive LLM-driven graph analysis.

Problem

• Enabling LLMs to analyze large-scale graphs effectively
• Overcoming context constraints and inflexible reasoning in graphs
• Providing scalable adaptive analysis via dynamic tool chaining

Innovation

• Dynamic tool chaining for large-scale graph analysis
• Progressive graph distillation via reinforcement learning
• Structure-aware adaptation using spectral properties and lightweight adapters