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.

翻译：目前GNN面向GNN的NAS方法侧重于寻找具有浅层和简单结构的不同层集成组件,这些组件受到“超高”问题的限制。为了进一步探讨GNN结构结构多样性和深度的好处,我们提议GNN的一代管道,配有一个新的两阶段搜索空间,目的是自动产生高性能,同时以块状方式转让深层GNN模型。与此同时,为了缓解“超高”问题,我们在我们的搜索空间中加入了多个灵活的剩余连接,并在基本的GNN层中应用身份图绘制。在搜索算法中,我们利用Epsilon-greedy探索战略和奖励重塑等深层次学习。关于现实世界数据集的广泛实验显示,我们生成的GNNN模型超越了现有的手工设计和NAS型模型。