Graph Neural Architecture Search with GPT-4
arxiv(2023)
摘要
Graph Neural Architecture Search (GNAS) has shown promising results in
automatically designing graph neural networks. However, GNAS still requires
intensive human labor with rich domain knowledge to design the search space and
search strategy. In this paper, we integrate GPT-4 into GNAS and propose a new
GPT-4 based Graph Neural Architecture Search method (GPT4GNAS for short). The
basic idea of our method is to design a new class of prompts for GPT-4 to guide
GPT-4 toward the generative task of graph neural architectures. The prompts
consist of descriptions of the search space, search strategy, and search
feedback of GNAS. By iteratively running GPT-4 with the prompts, GPT4GNAS
generates more accurate graph neural networks with fast convergence.
Experimental results show that embedding GPT-4 into GNAS outperforms the
state-of-the-art GNAS methods.
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