Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks. - Sitao Luan; Mingde Zhao 001; Xiao-Wen Chang; Doina Precup (NeurIPS, 2019)

Revisit Policy Optimization in Matrix Form. - Sitao Luan; Xiao-Wen Chang; Doina Precup (CoRR, 2019)

META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation. - Mingde Zhao 001; Sitao Luan; Ian Porada; Xiao-Wen Chang; Doina Precup (AAMAS, 2020)

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks. - Sitao Luan; Mingde Zhao 001; Chenqing Hua; Xiao-Wen Chang; Doina Precup (CoRR, 2020)

A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning. - Mingde Zhao 001; Zhen Liu 019; Sitao Luan; Shuyuan Zhang; Doina Precup; Yoshua Bengio (NeurIPS, 2021)

Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification? - Sitao Luan; Chenqing Hua; Qincheng Lu; Jiaqi Zhu; Mingde Zhao 001; Shuyuan Zhang; Xiao-Wen Chang; Doina Precup (CoRR, 2021)

Revisiting Heterophily For Graph Neural Networks. - Sitao Luan; Chenqing Hua; Qincheng Lu; Jiaqi Zhu; Mingde Zhao 001; Shuyuan Zhang; Xiao-Wen Chang; Doina Precup (NeurIPS, 2022)

When Do We Need GNN for Node Classification? - Sitao Luan; Chenqing Hua; Qincheng Lu; Jiaqi Zhu; Xiao-Wen Chang; Doina Precup (CoRR, 2022)

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networks. - Sitao Luan; Mingde Zhao 001; Chenqing Hua; Xiao-Wen Chang; Doina Precup (CoRR, 2022)

When Do We Need Graph Neural Networks for Node Classification? - Sitao Luan; Chenqing Hua; Qincheng Lu; Jiaqi Zhu; Xiao-Wen Chang; Doina Precup (COMPLEX NETWORKS (1), 2023)

Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks. - Sitao Luan; Mingde Zhao 001; Xiao-Wen Chang; Doina Precup (COMPLEX NETWORKS (1), 2023)

MUDiff: Unified Diffusion for Complete Molecule Generation. - Chenqing Hua; Sitao Luan; Minkai Xu; Zhitao Ying; Jie Fu; Stefano Ermon; Doina Precup (LoG, 2023)

When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability. - Sitao Luan; Chenqing Hua; Minkai Xu; Qincheng Lu; Jiaqi Zhu; Xiao-Wen Chang; Jie Fu; Jure Leskovec; Doina Precup (NeurIPS, 2023)

On Addressing the Limitations of Graph Neural Networks. - Sitao Luan (CoRR, 2023)

GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detection. - Qincheng Lu; Sitao Luan; Xiao-Wen Chang (GLOBECOM, 2024)

Flexible Diffusion Scopes With Parameterized Laplacian for Heterophilic Graph Learning. - Qincheng Lu; Jiaqi Zhu; Sitao Luan; Xiao-Wen Chang (LoG, 2024)

ReactZyme: A Benchmark for Enzyme-Reaction Prediction. - Chenqing Hua; Bozitao Zhong; Sitao Luan; Liang Hong; Guy Wolf; Doina Precup; Shuangjia Zheng (NeurIPS, 2024)

What Is Missing For Graph Homophily? Disentangling Graph Homophily For Graph Neural Networks. - Yilun Zheng; Sitao Luan; Lihui Chen 001 (NeurIPS, 2024)

Representation Learning on Heterophilic Graph with Directional Neighborhood Attention. - Qincheng Lu; Jiaqi Zhu; Sitao Luan; Xiao-Wen Chang (CoRR, 2024)

What Is Missing In Homophily? Disentangling Graph Homophily For Graph Neural Networks. - Yilun Zheng; Sitao Luan; Lihui Chen 001 (CoRR, 2024)

The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges. - Sitao Luan; Chenqing Hua; Qincheng Lu; Liheng Ma; Lirong Wu; Xinyu Wang; Minkai Xu; Xiao-Wen Chang; Doina Precup; Rex Ying; Stan Z. Li; Jian Tang 005; et al. (CoRR, 2024)

Are Heterophily-Specific GNNs and Homophily Metrics Really Effective? Evaluation Pitfalls and New Benchmarks. - Sitao Luan; Qincheng Lu; Chenqing Hua; Xinyu Wang; Jiaqi Zhu; Xiao-Wen Chang; Guy Wolf; Jian Tang 005 (CoRR, 2024)

EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics. - Chenqing Hua; Yong Liu; Dinghuai Zhang; Odin Zhang; Sitao Luan; Kevin K. Yang; Guy Wolf; Doina Precup; Shuangjia Zheng (CoRR, 2024)

Is Graph Convolution Always Beneficial For Every Feature? - Yilun Zheng; Xiang Li; Sitao Luan; Xiaojiang Peng; Lihui Chen (CoRR, 2024)

Rethinking Structure Learning For Graph Neural Networks. - Yilun Zheng; Zhuofan Zhang; Ziming Wang; Xiang Li; Sitao Luan; Xiaojiang Peng; Lihui Chen (CoRR, 2024)

FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis. - Yilun Zheng; Sha Li; Fangkun Wu; Yang Ziyi; Lin Hongchao; Zhichao Hu; Cai Xinjun; Ziming Wang; Jinxuan Chen; Sitao Luan; Jiahao Xu; Lihui Chen (ACL (Findings), 2025)

Graph Neural Networks Meet Probabilistic Graphical Models: A Survey. - Chenqing Hua; Sitao Luan; Qian Zhang; Jie Fu; Guy Wolf (ICASSP, 2025)

Let Your Features Tell The Differences: Understanding Graph Convolution By Feature Splitting. - Yilun Zheng; Xiang Li; Sitao Luan; Xiaojiang Peng; Lihui Chen (ICLR, 2025)

RL Fine-Tuning Heals OOD Forgetting in SFT. - Hangzhan Jin; Sitao Luan; Sicheng Lyu; Guillaume Rabusseau; Reihaneh Rabbany; Doina Precup; Mohammad Hamdaqa (CoRR, 2025)

Exploring Heterophily in Graph-level Tasks. - Qinhan Hou; Yilun Zheng; Xichun Zhang; Sitao Luan; Jing Tang (CoRR, 2025)

EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing. - Sicheng Lyu; Yu Gu; Xinyu Wang; Jerry Huang; Sitao Luan; Yufei Cui; Xiao-Wen Chang; Peng Lu (CoRR, 2025)

Less is More: Denoising Knowledge Graphs For Retrieval Augmented Generation. - Yilun Zheng; Dan Yang; Jie Li; Lin Shang; Lihui Chen; Jiahao Xu; Sitao Luan (CoRR, 2025)