LTE4G: Long-Tail Experts for Graph Neural Networks

LTE4G: Long-Tail Experts for Graph Neural Networks

22 August 2022

Papers citing "LTE4G: Long-Tail Experts for Graph Neural Networks"

19 / 19 papers shown

Title
Subgraph Federated Learning for Local Generalization Sungwon Kim Yoonho Lee Yunhak Oh Namkyeong Lee Sukwon Yun Junseok Lee Sein Kim Carl Yang Chanyoung Park FedML OOD 70 1 0 06 Mar 2025
Graph Size-imbalanced Learning with Energy-guided Structural Smoothing Jiawen Qin Pengfeng Huang Qingyun Sun Cheng Ji Xingcheng Fu Jianxin Li 26 0 0 23 Dec 2024
Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts Sukwon Yun Inyoung Choi Jie Peng Yangfan Wu J. Bao Qiyiwen Zhang Jiayi Xin Qi Long Tianlong Chen MoE 40 4 0 10 Oct 2024
Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System Sein Kim Hongseok Kang Seungyoon Choi Donghyun Kim Minchul Yang Chanyoung Park KELM 34 17 0 17 Apr 2024
Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks Arjun Subramonian Jian Kang Yizhou Sun AI4CE 22 4 0 04 Apr 2024
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges Wei Ju Siyu Yi Yifan Wang Zhiping Xiao Zhengyan Mao ... Senzhang Wang Xinwang Liu Xiao Luo Philip S. Yu Ming Zhang AI4CE 34 36 0 07 Mar 2024
Self-Guided Robust Graph Structure Refinement Yeonjun In Kanghoon Yoon Kibum Kim Kijung Shin Chanyoung Park AAML 19 7 0 19 Feb 2024
A Metadata-Driven Approach to Understand Graph Neural Networks Tinghong Li Qiaozhu Mei Jiaqi Ma AI4CE 8 5 0 30 Oct 2023
GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation Mingxuan Ju Tong Zhao Wenhao Yu Neil Shah Yanfang Ye 23 12 0 01 Oct 2023
Class-Imbalanced Graph Learning without Class Rebalancing Zhining Liu Ruizhong Qiu Zhichen Zeng Hyunsik Yoo David Zhou Zhe Xu Yada Zhu Kommy Weldemariam Jingrui He Hanghang Tong AI4CE 10 8 0 27 Aug 2023
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions Zemin Liu Yuan N. Li Nan-Fang Chen Qian Wang Bryan Hooi Bin He FaML 11 12 0 26 Aug 2023
S-Mixup: Structural Mixup for Graph Neural Networks Jung-Hyun Kim Sukwon Yun Chanyoung Park 11 2 0 16 Aug 2023
RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification Zhengyan Mao Wei Ju Yifang Qin Xiao Luo Ming Zhang 13 15 0 04 Aug 2023
Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization Haohui Wang Baoyu Jing Kaize Ding Yada Zhu Wei Cheng Si Zhang Yonghui Fan Liqing Zhang Dawei Zhou 32 5 0 17 May 2023
MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation Kibum Kim Dongmin Hyun Sukwon Yun Chanyoung Park 11 22 0 17 Apr 2023
Class-Imbalanced Learning on Graphs: A Survey Yihong Ma Yijun Tian Nuno Moniz Nitesh V. Chawla 22 18 0 09 Apr 2023
Knowledge Distillation on Graphs: A Survey Yijun Tian Shichao Pei Xiangliang Zhang Chuxu Zhang Nitesh V. Chawla 8 28 0 01 Feb 2023
BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition Boyan Zhou Quan Cui Xiu-Shen Wei Zhao-Min Chen 240 765 0 05 Dec 2019
SMOTE: Synthetic Minority Over-sampling Technique Nitesh V. Chawla Kevin W. Bowyer Lawrence Hall W. Kegelmeyer AI4TS 148 25,150 0 09 Jun 2011