KDD2023 Hot GNN Papers Full List
rockingdingo 2023-09-01 #KDD2023 #knowledge graph #GNN
This blog summarizes the latest research development of GNN papers published in KDD2023 conferences. This year there are total 9 papers related to GNN in KDD2023. Most of the authors' affiliations are top research institutes (Google Research, DeepMind, Meta FAIR) and universities (Stanford, Berkeley, MIT, CMU and others).
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- 1.PERT-GNN: Latency Prediction for Microservice-Based Cloud-Native Applications via Graph Neural Networks
- 2.Improving the Expressiveness of K-hop Message-Passing GNNs by Injecting Contextualized Substructure Information
- 3.MGNN: Graph Neural Networks Inspired by Distance Geometry Problem
- 4.Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization
- 5.QTIAH-GNN: Quantity and Topology Imbalance-Aware Heterogeneous Graph Neural Network for Bankruptcy Prediction
- 6.WinGNN: Dynamic Graph Neural Networks with Random Gradient Aggregation Window
- 7.DGI: An Easy and Efficient Framework for GNN Model Evaluation
- 8.MIDLG: Mutual Information based Dual Level GNN for Transaction Fraud Complaint Verification
- 9.Real Time Index and Search Across Large Quantities of GNN Experts For Low Latency Online Learning
Paper List
1.PERT-GNN: Latency Prediction for Microservice-Based Cloud-Native Applications via Graph Neural Networks
Da Sun Handason Tam (The Chinese University of Hong Kong), Yang Liu (Shanghai University), Huanle Xu (University of Macau), Siyue Xie (The Chinese University of Hong Kong), Wing Cheong Lau (The Chinese University of Hong Kong)
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2.Improving the Expressiveness of K-hop Message-Passing GNNs by Injecting Contextualized Substructure Information
Tianjun Yao (Mohamed bin Zayed University of Artificial Intelligence), Yingxu Wang (Mohamed bin Zayed University of Artificial Intelligence), Kun Zhang (Carnegie Mellon University), Shangsong Liang (Mohamed bin Zayed University of Artificial Intelligence)
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3.MGNN: Graph Neural Networks Inspired by Distance Geometry Problem
Guanyu Cui (Renmin University of China), Zhewei Wei (Renmin University of China)
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4.Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization
Kaixuan Chen (Zhejiang University), Shunyu Liu (Zhejiang University), Tongtian Zhu (Zhejiang University), Ji Qiao (China Electric Power Research Institute), Yun Su (State Grid Shanghai Municipal Electric Power Company), Yingjie Tian (State Grid Shanghai Municipal Electric Power Company), Tongya Zheng (Zhejiang University), Haofei Zhang (Zhejiang University), Zunlei Feng (Zhejiang University), Jingwen Ye (Zhejiang University), Mingli Song (Zhejiang University)
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5.QTIAH-GNN: Quantity and Topology Imbalance-Aware Heterogeneous Graph Neural Network for Bankruptcy Prediction
Yucheng Liu (University of Science and Technology of China), Zipeng Gao (University of Science and Technology of China), Xiangyang Liu (University of Science and Technology of China), Pengfei Luo (University of Science and Technology of China ), Yang Yang (Nanjing University of Science and Technology; The Hong Kong Polytechnic University), Hui Xiong (The Hong Kong University of Science and Technology (Guangzhou); The Hong Kong University of Science and Technology)
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6.WinGNN: Dynamic Graph Neural Networks with Random Gradient Aggregation Window
Yifan Zhu (Tsinghua University), Fangpeng Cong (Yanshan University), Dan Zhang (Tsinghua University), Wenwen Gong (Tsinghua University), Qika Lin (Xiâ??an Jiaotong University), Wenzheng Feng (Tsinghua University), Yuxiao Dong (Tsinghua University), Jie Tang (Tsinghua University)
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7.DGI: An Easy and Efficient Framework for GNN Model Evaluation
Peiqi Yin (AWS Shanghai AI Lab; Southern University of Science and Technology; The Chinese University of Hong Kong), Xiao Yan (Southern University of Science and Technology), Jinjing Zhou (TensorChord), Qiang Fu (George Washington University), Zhenkun Cai (AWS), James Cheng (The Chinese University of Hong Kong), Bo Tang (Southern University of Science and Technology), Minjie Wang (AWS)
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8.MIDLG: Mutual Information based Dual Level GNN for Transaction Fraud Complaint Verification
Wen Zheng (Institute of Computing Technology, University of Chinese Academy of Sciences), Bingbing Xu (Institute of Computing Technology, Chinese Academy of Sciences), Emiao Lu (Wechat Pay, Tencent), Yang Li (Institute of Computing Technology, Chinese Academy of Sciences), Qi Cao (Institute of Computing Technology, Chinese Academy of Sciences), Xuan Zong (Wechat Pay, Tencent), Huawei Shen (Institute of Computing Technology, Chinese Academy of Sciences)
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9.Real Time Index and Search Across Large Quantities of GNN Experts For Low Latency Online Learning
Johan Zhi Kang Kok (National University of Singapore), Sien Yi Tan (GrabTaxi Holdings), Bingsheng He (National University of Singapore), Zhen Zhang (National University of Singapore)
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