Graph Differentiable Architecture Search with Structure Learning

Proof A.1 W e firstly give Lemma 1: Lemma 1 The operation weights are caculated by a softmax function. The number of target node's intra-group neighbors is "S" indicates the setting of searching phase. "E" indicates the setting of evaluation phase. The hyper-parameter λ which controls the hidden feature smoothness is set to be 0 .125 . We show the variance of synthetic graph experiment in Table 1 to endorse our analysis in Section 3. The table shows that the variance of accuracy is relatively big in the experiment setting. However, all the results are average of 100 runs.