Data-Augmented Curriculum Graph Neural Architecture Search under Distribution Shifts

Graph neural architecture search (NAS) has achieved great success in designing architectures for graph data processing. However, distribution shifts pose great challenges for graph NAS, since the optimal searched architectures for the training graph data may fail to generalize to the unseen test graph data. The sole prior work tackles this problem by customizing architectures for each graph instance through learning graph structural information, but fails to consider data augmentation during training, which has been proven by existing works to be able to improve generalization. In this paper, we propose Data-augmented Curriculum Graph Neural Architecture Search (DCGAS), which learns an architecture customizer with good generalizability to data under distribution shifts. Specifically, we design an embedding-guided data generator, which can generate sufficient graphs for training to help the model better capture graph structural information. In addition, we design a two-factor uncertainty-based curriculum weighting strategy, which can evaluate the importance of data in enabling the model to learn key information in real-world distribution and reweight them during training. Experimental results on synthetic datasets and real datasets with distribution shifts demonstrate that our proposed method learns generalizable mappings and outperforms existing methods.