Exploring Self-training for Imbalanced Node Classification. - Xin Juan; Meixin Peng; Xin Wang 035 (ICONIP (5), 2021)

AutoAUG : Automatic Data Augmentation for Graph Neural Networks. - Xiao Liang; Xin Wang 035; Haotian Xue 001; Yintao Yang; Xin Juan; Ying Wang 009 (ACAI, 2022)

Similarity-based domain adaptation network. - Meixin Peng; Zhanshan Li; Xin Juan (Neurocomputing, 2022)

Negative samples selecting strategy for graph contrastive learning. - Rui Miao 003; Yintao Yang; Yao Ma 001; Xin Juan; Haotian Xue 001; Jiliang Tang; Ying Wang 009; Xin Wang 035 (Inf. Sci., 2022)

Graph prototypical contrastive learning. - Meixin Peng; Xin Juan; Zhanshan Li (Inf. Sci., 2022)

INS-GNN: Improving graph imbalance learning with self-supervision. - Xin Juan; Fengfeng Zhou; Wentao Wang 006; Wei Jin 009; Jiliang Tang; Xin Wang 035 (Inf. Sci., 2023)

Molecular Data Programming: Towards Molecule Pseudo-labeling with Systematic Weak Supervision. - Xin Juan; Kaixiong Zhou; Ninghao Liu; Tianlong Chen 001; Xin Wang 035 (CVPR, 2024)

Multi-strategy adaptive data augmentation for Graph Neural Networks. - Xin Juan; Xiao Liang; Haotian Xue 001; Xin Wang 035 (Expert Syst. Appl., 2024)

Label-guided graph contrastive learning for semi-supervised node classification. - Meixin Peng; Xin Juan; Zhanshan Li (Expert Syst. Appl., 2024)

Dynamic self-training with less uncertainty for graph imbalance learning. - Xin Juan; Meixin Peng; Xin Wang 035 (Expert Syst. Appl., 2025)

Graph Defense Diffusion Model. - Xin He 003; Wenqi Fan; Yili Wang 004; Chengyi Liu; Rui Miao 003; Xin Juan; Xin Wang 035 (CoRR, 2025)

Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space. - Xin He 003; Yili Wang 004; Wenqi Fan; Xu Shen 002; Xin Juan; Rui Miao 003; Xin Wang 035 (CoRR, 2025)