Are Graphs Useful for LLMs? A Comprehensive Survey of Graph-Enhanced Large Language Models

Le résumé fourni par la source

Large language models (LLMs) have demonstrated strong capabilities across diverse tasks such as language understanding, reasoning, planning, and code generation. However, their sequence-based architectures limit their ability to capture complex relational structures, long-range dependencies, and multi-hop interactions. Graphs, which explicitly model entities and relationships, provide a natural complement to LLMs by enabling structured representation, multi-hop reasoning, and improved knowledge grounding. This synergy has led to a growing body of research on graph-enhanced LLMs, which we refer to as Graph4LLM. In this survey, we present a systematic, pipeline-oriented review of Graph4LLM methods, categorizing them into three stages of the LLM pipeline: (1) the input phase, where graphs structure prompts and incorporate external knowledge; (2) the model phase, where graphs guide word-level representations and agent-level coordination; and (3) the output phase, where graphs support structured reasoning, planning, and verification. For each phase, we provide a detailed review of the key methods and techniques. We further present a broad range of application scenarios, organizing them into general and domain-specific applications, and highlight how Graph4LLM methods demonstrate strong potential across diverse tasks and real-world settings. Finally, we outline the challenges and future research directions for developing more efficient and interpretable solutions. Resources for Graph4LLM are available at https://github.com/BUPT-GAMMA/Awesome-Graph4LLM.

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