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.

翻译：图分类是许多现实多媒体应用中的关键任务，图中可表示图像、视频和社交网络等多类多媒体数据类型。先前研究主要将图神经网络（GNN）应用于类别分布平衡的场景中。然而，真实数据通常呈现长尾类别分布，导致GNN对头部类别存在偏倚，且对尾部类别的泛化能力受限。现有方法多聚焦于在模型训练过程中重新平衡不同类别，这未能显式引入新知识，且牺牲了头部类别的性能。为克服这些缺陷，我们提出名为检索增强混合网络（RAHNet）的新型框架，以解耦方式联合学习鲁棒特征提取器与无偏分类器。在特征提取器训练阶段，我们设计了图检索模块，通过搜索相关图来直接增强尾部类别的类内多样性。此外，我们创新性地优化了面向类别的监督对比损失函数以获得区分性表征，该损失更适用于长尾场景。在分类器微调阶段，我们采用两种权重正则化技术（即最大范数与权重衰减）对分类器权重进行平衡。在多个权威基准上的实验验证了所提方法相较于当前最优方法的优越性。