gSampler: General and Efficient GPU-based Graph Sampling for Graph Learning. - Ping Gong 009; Renjie Liu 001; Zunyao Mao; Zhenkun Cai; Xiao Yan 002; Cheng Li 001; Minjie Wang; Zhuozhao Li (SOSP, 2023)

MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale. - Haitian Jiang; Renjie Liu 001; Xiao Yan 002; Zhenkun Cai; Minjie Wang; David Wipf (CoRR, 2023)

MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy. - Haitian Jiang; Renjie Liu 001; Zengfeng Huang; Yichuan Wang 002; Xiao Yan 002; Zhenkun Cai; Minjie Wang; David Wipf (ICLR, 2025)

Adaptive Parallel Training for Graph Neural Networks. - Kaihao Ma; Renjie Liu 001; Xiao Yan 002; Zhenkun Cai; Xiang Song 003; Minjie Wang; Yichao Li; James Cheng (PPoPP, 2025)

DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training. - Renjie Liu 001; Yichuan Wang 002; Xiao Yan 002; Haitian Jiang; Zhenkun Cai; Minjie Wang; Bo Tang 016; Jinyang Li 001 (Proc. ACM Manag. Data, 2025)

PolyG: Effective and Efficient GraphRAG with Adaptive Graph Traversal. - Renjie Liu 001; Haitian Jiang; Xiao Yan 002; Bo Tang 016; Jinyang Li 001 (CoRR, 2025)