Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning. - Yuxiang Wang 013; Xiao Yan 002; Chuang Hu; Fangcheng Fu; Wentao Zhang 001; Hao Wang 013; Shuo Shang; Jiawei Jiang 001 (CoRR, 2023)

Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning. - Yuxiang Wang 013; Xiao Yan 002; Chuang Hu; Quanqing Xu; Chuanhui Yang; Fangcheng Fu; Wentao Zhang 001; Hao Wang 013; Bo Du 001; Jiawei Jiang 001 (ICDE, 2024)

Self-Supervised Learning for Graph Dataset Condensation. - Yuxiang Wang 013; Xiao Yan 002; Shiyu Jin; Hao Huang 001; Quanqing Xu; Qingchen Zhang 001; Bo Du 001; Jiawei Jiang 001 (KDD, 2024)

Hound: Hunting Supervision Signals for Few and Zero Shot Node Classification on Text-attributed Graph. - Yuxiang Wang 013; Xiao Yan 002; Shiyu Jin; Quanqing Xu; Chuanhui Yang; Yuanyuan Zhu 001; Chuang Hu; Bo Du 001; Jiawei Jiang 001 (CoRR, 2024)

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph. - Yuxiang Wang 013; Xiao Yan 002; Shiyu Jin; Quanqing Xu; Chuang Hu; Yuanyuan Zhu 001; Bo Du 001; Jia Wu; Jiawei Jiang 001 (IJCAI, 2025)

MTGRBoost: Boosting Large-scale Generative Recommendation Models in Meituan. - Yuxiang Wang 013; Xiao Yan 002; Chi Ma; Mincong Huang; Xiaoguang Li; Lei Yu; Chuan Liu; Ruidong Han; He Jiang; Bin Yin; Shangyu Chen; Fei Jiang; et al. (CoRR, 2025)

How Significant Are the Real Performance Gains? An Unbiased Evaluation Framework for GraphRAG. - Qiming Zeng; Xiao Yan 002; Hao Luo; Yuhao Lin; Yuxiang Wang 013; Fangcheng Fu; Bo Du 001; Quanqing Xu; Jiawei Jiang 001 (CoRR, 2025)