Generate or Re-Weight? A Mutual-Guidance Method for Class-Imbalanced Graphs. - Zhongying Zhao 001; Gen Liu 001; Qi Meng; Chao Li 022; Qingtian Zeng (IJCAI, 2025)

LeDA-GNN: Learnable dual augmentation for graph neural networks. - Gen Liu 001; Zhongying Zhao 001; Chao Li 022; Yanwei Yu (Expert Syst. Appl., 2025)

Improving the quality of Positive and Negative Samples based on Topological Analysis and Counterfactual Reasoning for Graph Contrastive Learning. - Xiaoyu Wang; Qiqi Zhang; Gen Liu 001; Zhongying Zhao 001 (Expert Syst. Appl., 2025)

Encoder augmentation for multi-task graph contrastive learning. - Xiaoyu Wang; Qiqi Zhang; Gen Liu 001; Zhongying Zhao 001; Hongzhi Cui (Neurocomputing, 2025)

GraphBSSN: A simple yet effective generative method for node classification in class-imbalanced graphs. - Qi Meng; Gen Liu 001; Guangkai Wu; Hui Zhou; Zhongying Zhao 001 (Knowl. Based Syst., 2025)

Boosting graph contrastive learning via adaptive graph augmentation and topology-feature-level homophily. - Shuo Sun; Zhongying Zhao 001; Gen Liu 001; Qiqi Zhang; Lingtao Su (Int. J. Mach. Learn. Cybern., 2025)