GraphLLM: Boosting Graph Reasoning Ability of Large Language Model

Abstract

The advancement of Large Language Models (LLMs) has remarkably pushed the boundaries towards artificial general intelligence (AGI), with their exceptional ability on understanding diverse types of information, including but not limited to images and audio. Despite this progress, a critical gap remains in empowering LLMs to proficiently understand and reason on graph data, which is ubiquitous in Big Data applications such as social networks, knowledge graphs, and molecular databases. Recent studies underscore LLMs’ underwhelming performance on fundamental graph reasoning tasks. In this paper, we endeavor to unearth the obstacles that impede LLMs in graph reasoning, pinpointing the common practice of converting graphs into natural language descriptions (Graph2Text) as a fundamental bottleneck. To overcome this impediment, we introduce GraphLLM, a pioneering end-to-end approach that synergistically integrates graph learning models with LLMs through a novel Dynamic Task Configuration System. This system employs a Hierarchical Graph Processing Pipeline that combines Local Structure Analyzers for node-level features with Global Pattern Synthesizers for graph-level understanding, enabling scalable processing of large-scale graph data. Our empirical evaluations across four fundamental graph reasoning tasks validate the effectiveness of GraphLLM. The results exhibit a substantial average accuracy enhancement of 54.44%, alongside a noteworthy context reduction of 96.45% across various graph reasoning tasks, demonstrating significant potential for Big Data graph analytics.