Graph Contrastive Representation Learning with Input-Aware and Cluster-Aware Regularization. - Jin Li 032; Bingshi Li; Qirong Zhang; Xinlong Chen; Xinyang Huang; Longkun Guo; Yang-Geng Fu (ECML/PKDD (2), 2023)

Semi-Supervised Node Classification via Semi-Global Graph Transformer Based on Homogeneity Augmentation. - Jin Li 032; Yisong Huang 002; Xinlong Chen; Yang-Geng Fu (Parallel Process. Lett., 2023)

Curriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-Smoothness in Deep GNNs. - Jin Li 032; Qirong Zhang; Shuling Xu; Xinlong Chen; Longkun Guo; Yang-Geng Fu (AAAI, 2024)

HopMAE: Self-supervised Graph Masked Auto-Encoders from a Hop Perspective. - Chenjunhao Shi; Jin Li 032; Jianzhi Zhuang; Xi Yao; Yisong Huang 002; Yang-Geng Fu (ICIC (LNAI 2), 2024)

Training Graph Transformers via Curriculum-Enhanced Attention Distillation. - Yisong Huang 002; Jin Li 032; Xinlong Chen; Yang-Geng Fu (ICLR, 2024)

DWSSA: Alleviating over-smoothness for deep Graph Neural Networks. - Qirong Zhang; Jin Li 032; Qingqing Ye 001; Yuxi Lin; Xinlong Chen; Yang-Geng Fu (Neural Networks, 2024)

Enhanced Graph Transformer: Multi-scale attention with Heterophilous Curriculum Augmentation. - Jianzhi Zhuang; Jin Li 032; Chenjunhao Shi; Xinyi Lin; Yang-Geng Fu (Knowl. Based Syst., 2025)

GSSCL: A framework for Graph Self-Supervised Curriculum Learning based on clustering label smoothing. - Yang-Geng Fu; Xinlong Chen; Shuling Xu; Jin Li 032; Xi Yao; Ziyang Huang; Ying-Ming Wang (Neural Networks, 2025)

Another Perspective of Over-Smoothing: Alleviating Semantic Over-Smoothing in Deep GNNs. - Jin Li 032; Qirong Zhang; Wenxi Liu; Antoni B. Chan; Yang-Geng Fu (IEEE Trans. Neural Networks Learn. Syst., 2025)