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Do We Really Need Graph Convolution During Training? Light Post-Training
  Graph-ODE for Efficient Recommendation
v1v2 (latest)

Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation

26 July 2024
Weizhi Zhang
Liangwei Yang
Zihe Song
Henry Peng Zou
Ke Xu
Liancheng Fang
Philip S. Yu
    GNN
ArXiv (abs)PDFHTML

Papers citing "Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation"

3 / 3 papers shown
Title
Lighter-X: An Efficient and Plug-and-play Strategy for Graph-based Recommendation through Decoupled Propagation
Lighter-X: An Efficient and Plug-and-play Strategy for Graph-based Recommendation through Decoupled PropagationProceedings of the VLDB Endowment (PVLDB), 2025
Yanping Zheng
Zhewei Wei
Frank de Hoog
Xu Chen
Hongteng Xu
Yuhang Ye
Jiadeng Huang
129
0
0
11 Oct 2025
LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation
Weizhi Zhang
Liangwei Yang
Wooseong Yang
Henry Peng Zou
Yuqing Liu
Ke Xu
Sourav Medya
Philip S. Yu
260
5
0
03 Mar 2025
Graph Neural Controlled Differential Equations For Collaborative Filtering
Graph Neural Controlled Differential Equations For Collaborative FilteringThe Web Conference (WWW), 2025
Ke Xu
Weizhi Zhang
Zihe Song
Yuanjie Zhu
Philip S. Yu
337
4
0
23 Jan 2025
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