Measuring and Improving the Use of Graph Information in Graph Neural Networks. - Yifan Hou; Jie Zhang 046; James Cheng; Kaili Ma 001; Richard T. B. Ma; Hongzhi Chen; Ming-Chang Yang (ICLR, 2020)

Understanding Graph Neural Networks from Graph Signal Denoising Perspectives. - Guoji Fu; Yifan Hou; Jie Zhang 046; Kaili Ma 001; Barakeel Fanseu Kamhoua; James Cheng (CoRR, 2020)

Rethinking Graph Regularization for Graph Neural Networks. - Han Yang 002; Kaili Ma 001; James Cheng (AAAI, 2021)

HyperGraph Convolution Based Attributed HyperGraph Clustering. - Barakeel Fanseu Kamhoua; Lin Zhang; Kaili Ma 001; James Cheng; Bo Li 001; Bo Han 003 (CIKM, 2021)

Improving Graph Representation Learning by Contrastive Regularization. - Kaili Ma 001; Haochen Yang; Han Yang 002; Tatiana Jin; Pengfei Chen; Yongqiang Chen 002; Barakeel Fanseu Kamhoua; James Cheng (CoRR, 2021)

Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient. - Shanchao Yang; Kaili Ma 001; Baoxiang Wang 001; Hongyuan Zha (CoRR, 2021)

Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. - Yongqiang Chen 002; Han Yang 002; Yonggang Zhang 003; Kaili Ma 001; Tongliang Liu; Bo Han 003; James Cheng (ICLR, 2022)

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs. - Yongqiang Chen 002; Yonggang Zhang 003; Yatao Bian; Han Yang 002; Kaili Ma 001; Binghui Xie; Tongliang Liu; Bo Han 003; James Cheng (NeurIPS, 2022)

Exact Shape Correspondence via 2D graph convolution. - Barakeel Fanseu Kamhoua; Lin Zhang; Yongqiang Chen 002; Han Yang 002; Kaili Ma 001; Bo Han 003; Bo Li 001; James Cheng (NeurIPS, 2022)

Invariance Principle Meets Out-of-Distribution Generalization on Graphs. - Yongqiang Chen 002; Yonggang Zhang 003; Han Yang 002; Kaili Ma 001; Binghui Xie; Tongliang Liu; Bo Han 003; James Cheng (CoRR, 2022)

Pareto Invariant Risk Minimization. - Yongqiang Chen 002; Kaiwen Zhou; Yatao Bian; Binghui Xie; Kaili Ma 001; Yonggang Zhang 003; Han Yang 002; Bo Han 003; James Cheng (CoRR, 2022)

Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization. - Yongqiang Chen 002; Kaiwen Zhou; Yatao Bian; Binghui Xie; Bingzhe Wu; Yonggang Zhang 003; Kaili Ma 001; Han Yang 002; Peilin Zhao; Bo Han 003; James Cheng (ICLR, 2023)

Calibrating and Improving Graph Contrastive Learning. - Kaili Ma 001; Garry Yang; Han Yang 002; Yongqiang Chen 002; James Cheng (Trans. Mach. Learn. Res., 2023)

Learning to Boost Resilience of Complex Networks via Neural Edge Rewiring. - Shanchao Yang; Kaili Ma 001; Baoxiang Wang 001; Tianshu Yu 001; Hongyuan Zha (Trans. Mach. Learn. Res., 2023)

GRACE: A General Graph Convolution Framework for Attributed Graph Clustering. - Barakeel Fanseu Kamhoua; Lin Zhang; Kaili Ma 001; James Cheng; Bo Li 001; Bo Han 003 (ACM Trans. Knowl. Discov. Data, 2023)

Solving the non-submodular network collapse problems via Decision Transformer. - Kaili Ma 001; Han Yang 002; Shanchao Yang; Kangfei Zhao; Lanqing Li; Yongqiang Chen 002; Junzhou Huang; James Cheng; Yu Rong 001 (Neural Networks, 2024)

Retrieval-Augmented Generation with Hierarchical Knowledge. - Haoyu Huang; Yongfeng Huang; Junjie Yang; Zhenyu Pan; Yongqiang Chen 002; Kaili Ma 001; Hongzhi Chen; James Cheng (CoRR, 2025)