  Current GNN-oriented NAS methods focus on the search for different layer
aggregate components with shallow and simple architectures, which are limited
by the 'over-smooth' problem. To further explore the benefits from structural
diversity and depth of GNN architectures, we propose a GNN generation pipeline
with a novel two-stage search space, which aims at automatically generating
high-performance while transferable deep GNN models in a block-wise manner.
Meanwhile, to alleviate the 'over-smooth' problem, we incorporate multiple
flexible residual connection in our search space and apply identity mapping in
the basic GNN layers. For the search algorithm, we use deep-q-learning with
epsilon-greedy exploration strategy and reward reshaping. Extensive experiments
on real-world datasets show that our generated GNN models outperforms existing
manually designed and NAS-based ones.
