From Anchors to Answers: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models

Understand

Enabling large language models (LLMs) to effectively process and reason with graph-structured data remains a significant challenge despite their remarkable success in natural language tasks.

• Current approaches either convert graph structures into verbose textual descriptions, consuming substantial computational resources, or employ complex graph neural networks as tokenizers, which introduce significant training overhead.
• To bridge this gap, we present NT-LLM, a novel framework with an anchor-based positional encoding scheme for graph representation.
• Our approach strategically selects reference nodes as anchors and encodes each node's position relative to these anchors, capturing essential topological information without the computational burden of existing methods.