Curriculum-GraphLLM: Joint Optimization of Architectures, Structures and Texts for Denoised Graph Neural Architecture Search.
This paper proposes Curriculum-GraphLLM, a novel graphLLM framework for joint optimization of architectures, structures, and texts for denoised graph neural architecture search to jointly optimize GNN architectures, graph structures, and textual semantics as a unified denoising process during architecture search.