Sang-Woong Lee

RPGCN: A relation-preserving graph convolutional network for enhanced node feature prediction in recommender systems

This work introduces RPGCN (Relation-Preserving Graph Convolutional Network), a novel hybrid framework that unifies early and intermediate fusion strategies with multi-branch Graph Attention Networks (GATs) for enhanced relation modeling.

ARFOR: adaptive random forest and owl optimization for energy-efficient routing in WSN-IoT

3D-VRTGR: A novel 3D virtual relay tunnel-based geographic routing for flying ad hoc networks

Simulation results under varying UAV density, mobility, and traffic load demonstrate that 3D-VRTGR consistently outperforms GPSR, UF-GPSR, and A-Geo and confirm the robustness and efficiency of 3D-VRTGR in highly dynamic FANET environments.

ASGRR: Adaptive Swarm-Guided Graph Policy Routing for energy-efficient WSN-IoT networks

This paper proposes ASGRR (Adaptive Swarm-Guided Graph Policy Routing), a novel hybrid routing framework that integrates Message Passing Neural Networks, Policy Gradient Reinforcement Learning (PGRL), and the Artificial Bee Colony algorithm in a self-adaptive hybrid form.