GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better

TL;DR

GraphSOS enhances LLMs' understanding of graphs by optimizing node/edge order and sampling subgraphs, leading to improved performance in graph reasoning tasks.

Contribution

The paper introduces GraphSOS, a framework with order selection and subgraph sampling modules, improving LLMs' reasoning on graph data through novel serialization and sampling strategies.

Findings

Improved node classification accuracy.

Enhanced graph question-answering performance.

Better generalization across datasets.

Abstract

The success of Large Language Models (LLMs) in various domains has led researchers to apply them to graph-related problems by converting graph data into natural language text. However, unlike graph data, natural language inherently has sequential order. We observe a counter-intuitive fact that when the order of nodes or edges in the natural language description of a graph is shuffled, despite describing the same graph, model performance fluctuates between high performance and random guessing. Additionally, due to LLMs' limited input context length, current methods typically randomly sample neighbors of target nodes as representatives of their neighborhood, which may not always be effective for accurate reasoning. To address these gaps, we introduce GraphSOS (Graph Sampling and Order Selection). This novel model framework features an Order Selector Module to ensure proper serialization order of the graph and a Subgraph Sampling Module to sample subgraphs with better structure for better reasoning. Furthermore, we propose Graph CoT obtained through distillation, and enhance LLM's reasoning and zero-shot learning capabilities for graph tasks through instruction tuning. Experiments on multiple datasets for node classification and graph question-answering demonstrate that GraphSOS improves LLMs' performance and generalization ability on graph tasks.