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Test-Time Training for Graph Neural Networks


Oct 17, 2022
Yiqi Wang, Chaozhuo Li, Wei Jin, Rui Li, Jianan Zhao, Jiliang Tang, Xing Xie

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Empowering Graph Representation Learning with Test-Time Graph Transformation


Oct 07, 2022
Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, Neil Shah

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Learning Representations for Hyper-Relational Knowledge Graphs


Aug 30, 2022
Harry Shomer, Wei Jin, Juanhui Li, Yao Ma, Jiliang Tang

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Condensing Graphs via One-Step Gradient Matching


Jun 15, 2022
Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, Bin Ying

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* 11 pages, KDD2022 

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Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective


Jun 15, 2022
Wei Jin, Xiaorui Liu, Yao Ma, Charu Aggarwal, Jiliang Tang

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* Accepted by KDD 2022 

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Identifying Critical LMS Features for Predicting At-risk Students


Apr 27, 2022
Ying Guo, Cengiz Gunay, Sairam Tangirala, David Kerven, Wei Jin, Jamye Curry Savage, Seungjin Lee

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Graph Neural Networks for Multimodal Single-Cell Data Integration


Mar 03, 2022
Hongzhi Wen, Jiayuan Ding, Wei Jin, Yuying Xie, Jiliang Tang

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* Submitted to KDD 2022 Applied Data Science Track 

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Graph Condensation for Graph Neural Networks


Oct 14, 2021
Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, Neil Shah

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* 16 pages, 4 figures 

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From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness


Oct 07, 2021
Lingxiao Zhao, Wei Jin, Leman Akoglu, Neil Shah

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* Expressive GNN framework 

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Graph Trend Networks for Recommendations


Aug 12, 2021
Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang, Qing Li

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