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Measuring and Improving the Use of Graph Information in Graph Neural Networks


Jun 27, 2022
Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard T. B. Ma, Hongzhi Chen, Ming-Chang Yang

* This paper has been published in ICLR 2020. Code and Dataset can be found here: https://github.com/yifan-h/CS-GNN 

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Pareto Invariant Risk Minimization


Jun 15, 2022
Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Kaili Ma, Yonggang Zhang, Han Yang, Bo Han, James Cheng

* A preprint version, 9 pages, 12 figures 

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Understanding and Improving Graph Injection Attack by Promoting Unnoticeability


Feb 16, 2022
Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng

* ICLR2022 

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Invariance Principle Meets Out-of-Distribution Generalization on Graphs


Feb 11, 2022
Yongqiang Chen, Yonggang Zhang, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng

* A preprint version 

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Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient


Oct 18, 2021
Shanchao Yang, Kaili Ma, Baoxiang Wang, Hongyuan Zha


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Improving Graph Representation Learning by Contrastive Regularization


Jan 27, 2021
Kaili Ma, Haochen Yang, Han Yang, Tatiana Jin, Pengfei Chen, Yongqiang Chen, Barakeel Fanseu Kamhoua, James Cheng


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Rethinking Graph Regularization For Graph Neural Networks


Sep 04, 2020
Han Yang, Kaili Ma, James Cheng


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Understanding Graph Neural Networks from Graph Signal Denoising Perspectives


Jun 08, 2020
Guoji Fu, Yifan Hou, Jian Zhang, Kaili Ma, Barakeel Fanseu Kamhoua, James Cheng

* 19 pages, 8 figures 

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