Aiming at two molecular graph datasets and one protein association subgraph dataset in OGB graph classification task, we design a graph neural network framework for graph classification task by introducing PAS(Pooling Architecture Search). At the same time, we improve it based on the GNN topology design method F2GNN to further design the feature selection and fusion strategies, so as to further improve the performance of the model in the graph property prediction task while overcoming the over smoothing problem of deep GNN training. Finally, a performance breakthrough is achieved on these three datasets, which is significantly better than other methods with fixed aggregate function. It is proved that the NAS method has high generalization ability for multiple tasks and the advantage of our method in processing graph property prediction tasks.

翻译：为了在OGB图表分类任务中实现两个分子图形数据集和一个蛋白质协会子集数据集,我们通过采用PAS(Pooling Proful Search)为图表分类任务设计了一个图形神经网络框架。与此同时,我们根据GNN的地形设计方法F2GNN改进了这一框架,以进一步设计特征选择和聚合战略,从而进一步改善图形属性预测任务模型的性能,同时克服深层GNN培训的平滑问题。最后,这三套数据集取得了业绩突破,大大优于固定总功能的其他方法。事实证明,NAS方法对于多项任务具有高度的概括性能力,而且我们在处理图形属性预测任务的方法具有优势。