Recent prevailing works on graph machine learning typically follow a similar
methodology that involves designing advanced variants of graph neural networks
(GNNs) to maintain the superior performance of GNNs on different graphs. In
this paper, we aim to streamline the GNN design process and leverage the
advantages of Large Language Models (LLMs) to improve the performance of GNNs
on downstream tasks. We formulate a new paradigm, coined "LLMs-as-Consultants,"
which integrates LLMs with GNNs in an interactive manner. A framework named
LOGIN (LLM Consulted GNN training) is instantiated, empowering the interactive
utilization of LLMs within the GNN training process. First, we attentively
craft concise prompts for spotted nodes, carrying comprehensive semantic and
topological information, and serving as input to LLMs. Second, we refine GNNs
by devising a complementary coping mechanism that utilizes the responses from
LLMs, depending on their correctness. We empirically evaluate the effectiveness
of LOGIN on node classification tasks across both homophilic and heterophilic
graphs. The results illustrate that even basic GNN architectures, when employed
within the proposed LLMs-as-Consultants paradigm, can achieve comparable
performance to advanced GNNs with intricate designs. Our codes are available at
this https URL

将大型语言模型（LLMs）与图神经网络（GNNs）相结合的互动方式（LLMs-as-Consultants）——LOGIN（LLM Consulted GNN training）框架，在节点分类任务上取得了与复杂设计的先进 GNNs 相媲美的性能，而且其基本 GNN 架构能够实现与强化设计的 GNNs 相当的性能。