Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision

arXiv:2403.05064v1 Announce Type: new
Abstract: The existing graph neural architecture search (GNAS) methods heavily rely on supervised labels during the search process, failing to handle ubiquitous scenarios where supervisions are not available. In this paper, we study the problem of unsupervised graph neural architecture search, which remains unexplored in the literature. The key problem is to discover the latent graph factors that drive the formation of graph data as well as the underlying relations between the factors and the optimal …

abstract architecture arxiv cs.ai cs.lg graph labels literature neural architecture search paper process search study supervision type unsupervised