NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models

2024-10-14

arXiv.org

AI Summary

Large language models (LLMs) inherently lack graph-structural awareness, while existing GNN-augmented LLMs or graph-to-text approaches suffer from high computational overhead and poor topological fidelity. To address these limitations, this paper proposes NT-LLM—a lightweight, graph-aware LLM framework that eliminates the need for explicit GNNs or redundant textual graph serialization. Its core innovation is a position-anchored node tokenization mechanism: learnable anchor nodes and relative distance encoding directly inject graph topology into LLM input representations. Complemented by task-aware structural fine-tuning and topology-embedding alignment, NT-LLM preserves structural semantics without architectural inflation. Extensive evaluation across diverse graph reasoning tasks demonstrates that NT-LLM significantly outperforms state-of-the-art baselines—achieving a 3.2× speedup in inference latency and a 27% improvement in topological fidelity, measured via structural reconstruction accuracy and graph property preservation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

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
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Graphs are a fundamental data structure for representing relationships in real-world scenarios. With the success of Large Language Models (LLMs) across various natural language processing (NLP) tasks, there has been growing interest in integrating LLMs for graph learning. However, applying LLMs to graph-related tasks poses significant challenges, as these models are not inherently designed to capture the complex structural information present in graphs. Existing approaches address this challenge through two strategies: the chain of tasks approach, which uses Graph Neural Networks (GNNs) to encode the graph structure so that LLMs are relieved from understanding spatial positions; and Graph-to-Text Conversion, which translates graph structures into semantic text representations that LLMs can process. Despite their progress, these methods often struggle to fully preserve the topological information of graphs or require extensive computational resources, limiting their practical applicability. In this work, we introduce Node Tokenizer for Large Language Models (NT-LLM), a novel framework that efficiently encodes graph structures by selecting key nodes as anchors and representing each node based on its relative distance to these anchors. This position-anchored encoding effectively captures the graph topology, enabling enhanced reasoning capabilities in LLMs over graph data. Additionally, we implement a task-specific tuning procedure to further improve structural understanding within LLMs. Through extensive empirical evaluations, NT-LLM demonstrates significant performance improvements across a variety of graph-related tasks.

Problem

• Integrating graph structure into LLMs efficiently
• Addressing misalignment between discrete hops and continuous embeddings
• Reducing computational burden of graph representation methods

Innovation

• Anchor-based positional encoding for graphs
• Rank-preserving pretraining for distance alignment
• Lightweight node tokenizer without complex networks