Efficient Graph Neural Architecture Search
Recently, graph neural networks (GNN) have been demonstrated effective in various graph-based tasks. To obtain state-of-the-art (SOAT) data-specific GNN architectures, researchers turn to the neural architecture search (NAS) methods. However, it remains to be a challenging problem to conduct efficient architecture search for GNN. In this work, we present a novel framework for Efficient GrAph Neural architecture search (EGAN). By designing a novel and expressive search space, an efficient one-shot NAS method based on stochastic relaxation and natural gradient is proposed. Further, to enable architecture search in large graphs, a transfer learning paradigm is designed. Extensive experiments, including node-level and graph-level tasks, are conducted. The results show that the proposed EGAN can obtain SOTA data-specific architectures, and reduce the search cost by two orders of magnitude compared to existing NAS baselines.
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