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：图分类是许多实际多媒体应用中的关键任务，其中图可以表示图像、视频和社交网络等多种多媒体数据类型。以往的研究在类别分布均衡的条件下应用图神经网络（GNNs）取得了成效。然而，现实数据通常呈现长尾类别分布，导致GNNs对头部类别产生偏差，而对尾部类别的泛化能力有限。近期方法主要侧重于在模型训练过程中重新平衡不同类别，但此类方法未能显式引入新知识，且牺牲了头部类别的性能。针对这些缺陷，我们提出一种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，我们设计了一个图检索模块，用于搜索相关图，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以类别为中心的监督对比损失，以获得更具判别性的表示，该方法更适用于长尾场景。在分类器微调阶段，我们采用两种权重正则化技术（即最大范数和权重衰减）来平衡分类器权重。在多个主流基准数据集上的实验验证了本方法相较于现有最先进方法的优越性。