将图结构转化为自然语言式序列，让大模型更好理解图数据。
Graph Linearization Methods for Reasoning on Graphs with Large Language Models
- 基于图中心性和可约性设计有语义的线性化方法
- 在多个图推理任务上显著优于随机线性化基线
- 适合希望用大模型处理图数据的研究者
大语言模型已具备处理文本之外多模态数据的能力，促使我们探索如何有效利用它们进行图推理。核心问题是将图转化为线性标记序列（即“图线性化”），使语言模型能自然处理图结构。我们提出应使线性化反映自然语言的局部依赖与全局对齐特性，以适应训练于万亿级文本的现有语言模型。为此，我们基于图中心性和可约性设计了多种线性化方法，并通过节点重命名进一步优化。实验表明，该方法在多个图推理任务中显著优于随机线性化基线。本工作提出适用于大模型的新图表示形式，推动图机器学习与统一变压器模型的多模态处理趋势融合。
原文摘要 · Abstract (English)
Large language models have evolved to process multiple modalities beyond text, such as images and audio, which motivates us to explore how to effectively leverage them for graph reasoning tasks. The key question, therefore, is how to transform graphs into linear sequences of tokens, a process we term "graph linearization", so that LLMs can handle graphs naturally. We consider that graphs should be linearized meaningfully to reflect certain properties of natural language text, such as local dependency and global alignment, in order to ease contemporary LLMs, trained on trillions of textual tokens, better understand graphs. To achieve this, we developed several graph linearization methods based on graph centrality and degeneracy. These methods are further enhanced using node relabeling techniques. The experimental results demonstrate the effectiveness of our methods compared to the random linearization baseline. Our work introduces novel graph representations suitable for LLMs, contributing to the potential integration of graph machine learning with the trend of multimodal processing using a unified transformer model.
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