Efficient Graph Understanding with LLMs via Structured Context Injection

2025-08-31

AI Summary

Large language models (LLMs) struggle to comprehend complex graph structures and perform conceptual graph reasoning without fine-tuning. Method: This paper proposes a structured context injection framework that requires no parameter modification. Leveraging prompt engineering and structured input construction, it maps graph tasks into a conceptual representation space, implicitly aligning graph topology with semantics at the input layer—eliminating the need for multi-step querying or costly fine-tuning. Contribution/Results: The framework is compatible with both lightweight and large-scale LLMs, achieving significant accuracy gains across multiple graph reasoning benchmarks. Its performance matches or surpasses state-of-the-art fine-tuned methods while drastically reducing computational overhead. It offers high efficiency, broad applicability across diverse graph tasks, and seamless scalability to larger models and datasets.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• 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: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Large Language Models (LLMs) have shown strong capabilities in solving problems across domains, including graph-related tasks traditionally addressed by symbolic or algorithmic methods. In this work, we present a framework for structured context injection, where task-specific information is systematically embedded in the input to guide LLMs in solving a wide range of graph problems. Our method does not require fine-tuning of LLMs, making it cost-efficient and lightweight. We observe that certain graph reasoning tasks remain challenging for LLMs unless they are mapped to conceptually grounded representations. However, achieving such mappings through fine-tuning or repeated multi-step querying can be expensive and inefficient. Our approach offers a practical alternative by injecting structured context directly into the input, enabling the LLM to implicitly align the task with grounded conceptual spaces. We evaluate the approach on multiple graph tasks using both lightweight and large models, highlighting the trade-offs between accuracy and computational cost. The results demonstrate consistent performance improvements, showing that structured input context can rival or surpass more complex approaches. Our findings underscore the value of structured context injection as an effective and scalable strategy for graph understanding with LLMs.

Problem

• Enhancing graph reasoning tasks using structured context injection
• Enabling LLMs to solve graph problems without fine-tuning
• Improving accuracy and efficiency in graph understanding with LLMs

Innovation

• Structured context injection without fine-tuning
• Direct input embedding for graph alignment
• Lightweight framework for efficient graph understanding