  Neural Architecture Search (NAS) automates and prospers the design of neural
networks. Estimator-based NAS has been proposed recently to model the
relationship between architectures and their performance to enable scalable and
flexible search. However, existing estimator-based methods encode the
architecture into a latent space without considering graph similarity. Ignoring
graph similarity in node-based search space may induce a large inconsistency
between similar graphs and their distance in the continuous encoding space,
leading to inaccurate encoding representation and/or reduced representation
capacity that can yield sub-optimal search results. To preserve graph
correlation information in encoding, we propose NASGEM which stands for Neural
Architecture Search via Graph Embedding Method. NASGEM is driven by a novel
graph embedding method equipped with similarity measures to capture the graph
topology information. By precisely estimating the graph distance and using an
auxiliary Weisfeiler-Lehman kernel to guide the encoding, NASGEM can utilize
additional structural information to get more accurate graph representation to
improve the search efficiency. GEMNet, a set of networks discovered by NASGEM,
consistently outperforms networks crafted by existing search methods in
classification tasks, i.e., with 0.4%-3.6% higher accuracy while having 11%-
21% fewer Multiply-Accumulates. We further transfer GEMNet for COCO object
detection. In both one-stage and twostage detectors, our GEMNet surpasses its
manually-crafted and automatically-searched counterparts.
