RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification

• URL: http://arxiv.org/abs/2308.02335v2
• Date: Thu, 7 Sep 2023 07:46:16 GMT
• Title: RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification
• Authors: Zhengyang Mao, Wei Ju, Yifang Qin, Xiao Luo, and Ming Zhang
• Abstract summary: We propose a novel framework called Retrieval Augmented Hybrid Network (RAHNet) to jointly learn a robust feature extractor and an unbiased classifier. 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. We also innovatively optimize a category-centered supervised contrastive loss to obtain discriminative representations.
• Score: 10.806893809269074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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