7 citations · 14 across the 9 of their papers we have counts for
9 papers
Real-time prediction of breast cancer sites using deformation-aware graph neural network
Kyunghyun Lee, Yong-Min Shin, Minwoo Shin +4
Early diagnosis of breast cancer is crucial, enabling the establishment of appropriate treatment plans and markedly enhancing patient prognosis. While direct magnetic resonance ima…
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…
Faithful and Accurate Self-Attention Attribution for Message Passing Neural Networks via the Computation Tree Viewpoint
Yong-Min Shin, Siqing Li, Xin Cao +1
The self-attention mechanism has been adopted in various popular message passing neural networks (MPNNs), enabling the model to adaptively control the amount of information that fl…
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…
Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs
Yong-Min Shin, Won-Yong Shin
Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve semisupervised node classification on graphs, by training a student MLP by knowledge distillation from a…
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…
PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks
Yong-Min Shin, Sun-Woo Kim, Won-Yong Shin
Aside from graph neural networks (GNNs) attracting significant attention as a powerful framework revolutionizing graph representation learning, there has been an increasing demand…
Time-Series Anomaly Detection with Implicit Neural Representation
Kyeong-Joong Jeong, Yong-Min Shin
Detecting anomalies in multivariate time-series data is essential in many real-world applications. Recently, various deep learning-based approaches have shown considerable improvem…
Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes
Yong-Min Shin, Cong Tran, Won-Yong Shin +1
We study the problem of embedding edgeless nodes such as users who newly enter the underlying network, while using graph neural networks (GNNs) widely studied for effective represe…