GraphChain: Large Language Models for Large-scale Graph Analysis via Tool Chaining
This work addresses scalability and adaptability issues in LLM-driven graph analysis for researchers and practitioners, though it appears incremental by building on existing tool-chaining concepts.
The paper tackled the problem of applying Large Language Models (LLMs) to large-scale graph analysis by developing GraphChain, a framework that uses dynamic tool sequences to overcome context constraints and inflexible reasoning, resulting in significant performance improvements over prior methods.
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.