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Existing neural architecture search (NAS) methods often operate in discrete or continuous spaces directly, which ignores the graphical topology knowledge of neural networks. This leads to suboptimal search performance and efficiency, given the factor that neural networks are essentially directed acyclic graphs (DAG). In this work, we address this limitation by introducing a novel idea of neural graph embedding (NGE). Specifically, we represent the building block (i.e. the cell) of neuraldoi:10.1609/aaai.v34i04.5903 fatcat:iphvazyxbbhirkhh5cop2e7tya