publications

2025

1. AAAI

   Faithful and Accurate Self-Attention Attribution for Message Passing Neural Networks via the Computation Tree Viewpoint

   Yong-Min Shin, Siqing Li, Xin Cao, and Won-Yong Shin

   In AAAI, Feb 2025

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2024

1. IJCAIW

   On the Feasibility of Fidelity⁻ for Graph Pruning

   Yong-Min Shin, and Won-Yong Shin

   In IJCAI Workshop on Explainable Artificial Intelligence (XAI), Aug 2024

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2. SIGIR

   Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation

   Jin-Duk Park, Yong-Min Shin, and Won-Yong Shin

   In ACM SIGIR Conference on Research and Development in Information Retrieval, Apr 2024

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3. PAMI

   PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks

   Yong-Min Shin, Sun-Woo Kim, and Won-Yong Shin

   Transactions on Pattern Analysis and Machine Intelligence, Apr 2024

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4. arXiv

   KAN: Kolmogorov-Arnold Networks

   Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacic, Thomas Y. Hou, and Max Tegmark

   CoRR, Apr 2024

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2023

1. LoG

   Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs

   Yong-Min Shin, and Won-Yong Shin

   In The Second Learning on Graphs Conference, Nov 2023

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2022

1. AAAI

   Prototype-Based Explanations for Graph Neural Networks (Student Abstract) (selected for oral presentation)

   Yong-Min Shin, Sun-Woo Kim, Eun-Bi Yoon, and Won-Yong Shin

   In AAAI Conference on Artificial Intelligence, (AAAI), Feb 2022

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2. arXiv

   Time-Series Anomaly Detection with Implicit Neural Representation

   Kyeong-Joong Jeong, and Yong-Min Shin

   arXiv preprint, Feb 2022

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3. TETC

   Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes

   Yong-Min Shin, Cong Tran, Won-Yong Shin, and Xin Cao

   IEEE Transactions on Emerging Topics in Computing, Jul 2022

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   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 representation learning of graphs. Our study is motivated by the fact that GNNs cannot be straightforwardly adopted for our problem since message passing to such edgeless nodes having no connections is impossible. To tackle this challenge, we propose Edgeless-GNN, a novel inductive framework that enables GNNs to generate node embeddings even for edgeless nodes through unsupervised learning. Specifically, we start by constructing a proxy graph based on the similarity of node attributes as the GNN's computation graph defined by the underlying network. The known network structure is used to train model parameters, whereas a topology-aware loss function is established such that our model judiciously learns the network structure by encoding positive, negative, and second-order relations between nodes. For the edgeless nodes, we inductively infer embeddings by expanding the computation graph. By evaluating the performance of various downstream machine learning tasks, we empirically demonstrate that Edgeless-GNN exhibits (a) superiority over state-of-the-art inductive network embedding methods for edgeless nodes, (b) effectiveness of our topology-aware loss function, (c) robustness to incomplete node attributes, and (d) a linear scaling with the graph size.

4. PLOS ONE

   Explainable gait recognition with prototyping encoder–decoder

   Jucheol Moon, Yong-Min Shin, Jin-Duk Park, Nelson Hebert Minaya, Won-Yong Shin, and Sang-Il Choi

   PLOS ONE, Mar 2022

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   Human gait is a unique behavioral characteristic that can be used to recognize individuals. Collecting gait information widely by the means of wearable devices and recognizing people by the data has become a topic of research. While most prior studies collected gait information using inertial measurement units, we gather the data from 40 people using insoles, including pressure sensors, and precisely identify the gait phases from the long time series using the pressure data. In terms of recognizing people, there have been a few recent studies on neural network-based approaches for solving the open set gait recognition problem using wearable devices. Typically, these approaches determine decision boundaries in the latent space with a limited number of samples. Motivated by the fact that such methods are sensitive to the values of hyper-parameters, as our first contribution, we propose a new network model that is less sensitive to changes in the values using a new prototyping encoder–decoder network architecture. As our second contribution, to overcome the inherent limitations due to the lack of transparency and interpretability of neural networks, we propose a new module that enables us to analyze which part of the input is relevant to the overall recognition performance using explainable tools such as sensitivity analysis (SA) and layer-wise relevance propagation (LRP).

5. ICLR

   The Efficiency Misnomer

   Mostafa Dehghani, Yi Tay, Anurag Arnab, Lucas Beyer, and Ashish Vaswani

   In ICLR, Apr 2022

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2021

1. ICLR

   On the Bottleneck of Graph Neural Networks and its Practical Implications

   Uri Alon, and Eran Yahav

   In ICLR, May 2021

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2019

1. arXiv

   Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

   Hoang NT, and Takanori Maehara

   CoRR, May 2019

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2. ICML

   Simplifying Graph Convolutional Networks

   Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger

   In ICML, Jun 2019

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2017

1. ICLR

   Semi-Supervised Classification with Graph Convolutional Networks

   Thomas N. Kipf, and Max Welling

   In ICLR, Apr 2017

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2. NeurIPS

   Inductive Representation Learning on Large Graphs

   William L. Hamilton, Zhitao Ying, and Jure Leskovec

   In NeurIPS, Dec 2017

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2016

1. ICML

   Revisiting Semi-Supervised Learning with Graph Embeddings

   Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov

   In ICML 2016, Jun 2016

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2. NeurIPS

   Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

   Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst

   In NeurIPS, Dec 2016

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2013

1. The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains

   David I. Shuman, Sunil K. Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst

   IEEE Signal Process. Mag., Dec 2013

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2007

1. A tutorial on spectral clustering

   Ulrike Luxburg

   Stat. Comput., Dec 2007

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2003

1. NIPS

   Learning with Local and Global Consistency

   Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Schölkopf

   In NIPS, Dec 2003

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