  Graph classification is a crucial task in many real-world multimedia
applications, where graphs can represent various multimedia data types such as
images, videos, and social networks. Previous efforts have applied graph neural
networks (GNNs) in balanced situations where the class distribution is
balanced. However, real-world data typically exhibit long-tailed class
distributions, resulting in a bias towards the head classes when using GNNs and
limited generalization ability over the tail classes. Recent approaches mainly
focus on re-balancing different classes during model training, which fails to
explicitly introduce new knowledge and sacrifices the performance of the head
classes. To address these drawbacks, we propose a novel framework called
Retrieval Augmented Hybrid Network (RAHNet) to jointly learn a robust feature
extractor and an unbiased classifier in a decoupled manner. In the feature
extractor training stage, we develop a graph retrieval module to search for
relevant graphs that directly enrich the intra-class diversity for the tail
classes. Moreover, we innovatively optimize a category-centered supervised
contrastive loss to obtain discriminative representations, which is more
suitable for long-tailed scenarios. In the classifier fine-tuning stage, we
balance the classifier weights with two weight regularization techniques, i.e.,
Max-norm and weight decay. Experiments on various popular benchmarks verify the
superiority of the proposed method against state-of-the-art approaches.
