Graphs with abundant attributes are essential in modeling interconnected entities and improving predictions in various real-world applications. Traditional Graph Neural Networks (GNNs), which are commonly used for modeling attributed graphs, need to be re-trained every time when applied to different graph tasks and datasets. Although the emergence of Large Language Models (LLMs) has introduced a new paradigm in natural language processing, the generative potential of LLMs in graph mining remains largely under-explored. To this end, we propose a novel framework MuseGraph, which seamlessly integrates the strengths of GNNs and LLMs and facilitates a more effective and generic approach for graph mining across different tasks and datasets. Specifically, we first introduce a compact graph description via the proposed adaptive input generation to encapsulate key information from the graph under the constraints of language token limitations. Then, we propose a diverse instruction generation mechanism, which distills the reasoning capabilities from LLMs (e.g., GPT-4) to create task-specific Chain-of-Thought-based instruction packages for different graph tasks. Finally, we propose a graph-aware instruction tuning with a dynamic instruction package allocation strategy across tasks and datasets, ensuring the effectiveness and generalization of the training process. Our experimental results demonstrate significant improvements in different graph tasks, showcasing the potential of our MuseGraph in enhancing the accuracy of graph-oriented downstream tasks while keeping the generation powers of LLMs.

提出了一种新颖的框架 MuseGraph，它无缝地整合了 GNN 和 LLM 的优势，通过自适应输入生成和多样化的指令生成机制，以及针对不同任务和数据集的图感知指令调优，提高了图任务的准确性，并保持了 LLM 的生成能力。

MuseGraph: 面向通用图挖掘的大型语言模型的图导向指令调优