3 papers
cs.LG2024
On the Feasibility of Fidelity for Graph Pruning
Yong-Min Shin, Won-Yong Shin
As one of popular quantitative metrics to assess the quality of explanation of graph neural networks (GNNs), fidelity measures the output difference after removing unimportant part…
cs.IR2024
Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation
Jin-Duk Park, Yong-Min Shin, Won-Yong Shin
A series of graph filtering (GF)-based collaborative filtering (CF) showcases state-of-the-art performance on the recommendation accuracy by using a low-pass filter (LPF) without a…
cs.LG2023
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs
Yong-Min Shin, Won-Yong Shin
Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve semi-supervised node classification on graphs, by training a student MLP by knowledge distillation (KD) f…