  Neural architecture search enables automation of architecture design. Despite
its success, it is computationally costly and does not provide an insight on
how to design a desirable architecture. Here we propose a new way of searching
neural network where we search neural architecture by rewiring the
corresponding graph and predict the architecture performance by graph
properties. Because we do not perform machine learning over the entire graph
space and use predicted architecture performance to search architecture, the
searching process is remarkably efficient. We find graph based search can give
a reasonably good prediction of desirable architecture. In addition, we find
graph properties that are effective to predict architecture performance. Our
work proposes a new way of searching neural architecture and provides insights
on neural architecture design.
