FairHELP: Fairness-Aware Heterogeneous Information Network Embedding for Link Prediction. - Meng Cao 004; Jianqing Song; Jinliang Yuan; Baoming Zhang; Chongjun Wang (DASFAA (3), 2023)

A Flexible Debiasing Framework for Fair Heterogeneous Information Network Embedding. - Meng Cao 004; Mingcai Chen; Jianqing Song; Chen-Xuan Fang; Chongjun Wang (ECAI, 2023)

Self-supervised robust Graph Neural Networks against noisy graphs and noisy labels. - Jinliang Yuan; Hualei Yu; Meng Cao 004; Jianqing Song; Junyuan Xie; Chongjun Wang (Appl. Intell., 2023)

Seeking Similarities While Removing Differences: Graph Neural Networks Based on Node Correlation. - Shuangjie Li; Baoming Zhang; Jianqing Song; Yifan Xia; Junyuan Xie; Chongjun Wang (ICASSP, 2024)

Similarity-Navigated Conformal Prediction for Graph Neural Networks. - Jianqing Song; Jianguo Huang; Wenyu Jiang; Baoming Zhang; Shuangjie Li; Chongjun Wang (NeurIPS, 2024)

Graph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise. - Shuangjie Li; Baoming Zhang; Jianqing Song; Gaoli Ruan; Chongjun Wang; Junyuan Xie (CoRR, 2024)

Normalize Then Propagate: Efficient Homophilous Regularization for Few-Shot Semi-Supervised Node Classification. - Baoming Zhang; Mingcai Chen; Jianqing Song; Shuangjie Li; Jie Zhang; Chongjun Wang (AAAI, 2025)

Graph Neural Networks with Coarse- and Fine-Grained Division for mitigating label noise and sparsity. - Shuangjie Li; Baoming Zhang; Jianqing Song; Gaoli Ruan; Chongjun Wang; Junyuan Xie (Neural Networks, 2025)