CDCGAN: Class Distribution-aware Conditional GAN-based minority augmentation for imbalanced node classification.

Autor: Liu B; School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address: liubojia@sjtu.edu.cn., Zheng C; School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai, 200240, China. Electronic address: chzheng@sjtu.edu.cn., Sun F; Information Technology Service Center of People's Court, Beijing, 100745, China. Electronic address: sunfh6732@163.com., Wang X; Information Technology Service Center of People's Court, Beijing, 100745, China. Electronic address: 428163395@139.com., Pan L; School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai, 200240, China; Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, 201203, China. Electronic address: panli@sjtu.edu.cn.
Jazyk: angličtina
Zdroj: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2024 Nov 29; Vol. 183, pp. 106933. Date of Electronic Publication: 2024 Nov 29.
DOI: 10.1016/j.neunet.2024.106933
Abstrakt: Node classification is a fundamental task of Graph Neural Networks (GNNs). However, GNN models tend to suffer from the class imbalance problem which deteriorates the representation ability of minority classes, thus leading to unappealing classification performance. The most straightforward and effective solution is to augment the minority samples for balancing the representations of majority and minority classes. Previous methods leverage a limited number of labeled nodes to generate new samples, without considering the overall class characteristics and failing to reflect the underlying class distributions. Besides, they often yield less distinguishable nodes that cannot represent their original classes well, because they may incorporate useless information from other classes to form node representations. To address this issue, we propose a Class Distribution-aware Conditional Generative Adversarial Network (CDCGAN) to generate diverse and distinguishable minority nodes based on their class distribution characteristics. Specifically, we extract the node embeddings and class distributions while preserving the topology and attribute information, thus capturing the overall class characteristics. Then, the obtained class distributions are used to design a conditional generator, which incorporates nonlinear transformations to generate diverse minority nodes and leverages adversarial learning to maintain intrinsic class distribution characteristics. At last, to ensure the distinguishability of node representations, a unique discriminator is implemented to jointly discriminate and classify nodes of the augmented graph. Extensive experiments conducted on six datasets demonstrate that the proposed CDCGAN outperforms the state-of-the-art methods on widely used evaluation metrics. The source code is available at https://github.com/Crystal-LiuBojia/CDCGAN.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: