UnsupervisedGraphNeuralArchitectureSearch withDisentangledSelf-supervision

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 inthe 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 andtheoptimal neural architectures.