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A federated learning framework for arbitrary spatio-temporal graph neural networks

Article
A federated learning framework for arbitrary spatio-temporal graph neural networks
  • Yoon, Heeyong ; 
  • Chon, Kang-Wook ; 
  • Kim, Min-Soo
  • 2025-12
  • ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v.162
  • PERGAMON-ELSEVIER SCIENCE LTD
  • View : 175
  • Download : 10
Conference
FedSTGNN: A Federated Spatio-Temporal Graph Neural Network
  • Yoon, Heeyong ; 
  • Chon, Kang-Wook ; 
  • Kim, Min-Soo
  • 2024-10-18
  • Yoon, Heeyong. (2024-10-18). FedSTGNN: A Federated Spatio-Temporal Graph Neural Network. 15th International Conference on Information and Communication Technology Convergence, ICTC 2024, 1863–1868. doi: 10.1109/ICTC62082.2024.10826762
  • IEEE Computer Society
  • View : 358
  • Download : 0
Conference
GFlux: A Fast GPU-Based Out-of-Memory Multi-Hop Query Processing Framework for Trillion-Edge Graphs
  • Oh, Seyeon ; 
  • Yoon, Heeyong ; 
  • Han, Donghyoung ; 
  • Kim, Min-soo
  • 2025-05-22
  • IEEE International Conference on Data Engineering, pp.932 - 945
  • IEEE Computer Society
  • View : 115
  • Download : 0

SearchLight: Neural Architecture Search for Lightweight Spatio-Temporal Graph Neural Networks

Article
SearchLight: Neural Architecture Search for Lightweight Spatio-Temporal Graph Neural Networks
  • Yoon, Heeyong ; 
  • Jung, Jinhong ; 
  • Chon, Kang-Wook ; 
  • Kim, Min-Soo
  • 2025-09
  • IEEE ACCESS, v.13, pp.165909 - 165926
  • IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
  • View : 70
  • Download : 26
Article
SPATS: a practical system for comparative analysis of spatio-temporal graph neural networks
  • Yoon, Heeyong ; 
  • Chon, Kang-Wook ; 
  • Kim, Min-Soo
  • 2025-09
  • Cluster Computing, v.28, no.13
  • Baltzer Science Publishers B.V.
  • View : 57
  • Download : 0
Article
Tweaking Deep Neural Networks
  • Kim, Jinwook ; 
  • Yoon, Heeyong ; 
  • Kim, Min-Soo
  • 2022-09
  • Kim, Jinwook. (2022-09). Tweaking Deep Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9), 1–1. doi: 10.1109/TPAMI.2021.3079511
  • IEEE Computer Society
  • View : 496
  • Download : 0
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