Heterogeneous graph neural architecture search (HGNAS) represents a powerful
tool for automatically designing effective heterogeneous graph neural networks.
However, existing HGNAS algorithms suffer from inefficient searches and
unstable results. In this paper, we present a new GPT-4 based HGNAS model to
improve the search efficiency and search accuracy of HGNAS. Specifically, we
present a new GPT-4 enhanced Heterogeneous Graph Neural Architecture Search
(GHGNAS for short). The basic idea of GHGNAS is to design a set of prompts that
can guide GPT-4 toward the task of generating new heterogeneous graph neural
architectures. By iteratively asking GPT-4 with the prompts, GHGNAS continually
validates the accuracy of the generated HGNNs and uses the feedback to further
optimize the prompts. Experimental results show that GHGNAS can design new
HGNNs by leveraging the powerful generalization capability of GPT-4. Moreover,
GHGNAS runs more effectively and stably than previous HGNAS models based on
reinforcement learning and differentiable search algorithms.

通过使用 GPT-4 模型，本论文提出了一种新的 GPT-4 增强的异构图神经网络结构搜索 (GHGNAS)，以提高搜索效率和准确性，并通过实验证明 GHGNAS 可以利用 GPT-4 的强大泛化能力，运行更有效和更稳定。