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• W3197529899 abstract "Graph neural networks (GNNs) have been successfully applied to learning representation on graphs in many relational tasks. Recently, researchers study neural architecture search (NAS) to reduce the dependence of human expertise and explore better GNN architectures, but they over-emphasize entity features and ignore latent relation information concealed in the edges. To solve this problem, we incorporate edge features into graph search space and propose Edge-featured Graph Neural Architecture Search to find the optimal GNN architecture. Specifically, we design rich entity and edge updating operations to learn high-order representations, which convey more generic message passing mechanisms. Moreover, the architecture topology in our search space allows to explore complex feature dependence of both entities and edges, which can be efficiently optimized by differentiable search strategy. Experiments at three graph tasks on six datasets show EGNAS can search better GNNs with higher performance than current state-of-the-art human-designed and searched-based GNNs." @default.
• W3197529899 created "2021-09-13" @default.
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• W3197529899 date "2021-09-03" @default.
• W3197529899 modified "2023-09-23" @default.
• W3197529899 title "Edge-featured Graph Neural Architecture Search" @default.
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• W3197529899 doi "https://doi.org/10.48550/arxiv.2109.01356" @default.
• W3197529899 hasPublicationYear "2021" @default.
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