Publications

Please visit https://jaeminyoo.github.io for full papers, code, and datasets.

2025

[c27] TSA on AutoPilot: Self-tuning Self-supervised Time Series Anomaly Detection
Boje Deforce, Meng-Chieh Lee, Bart Baesens, Estefanía Serral Asensio, Jaemin Yoo and Leman Akoglu
SIAM International Conference on Data Mining (SDM) 2025

2024

[c26] Rethinking Reconstruction-based Graph-level Anomaly Detectors: Limitations and a Remedy
Sunwoo Kim, Soo Yong Lee, Fanchen Bu, Shinhwan Kang, Kyungho Kim, Jaemin Yoo, and Kijung Shin
Conference on Neural Information Processing Systems (NeurIPS) 2024

[c25] Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective
Soo Yong Lee, Sunwoo Kim, Fanchen Bu, Jaemin Yoo, Jiliang Tang, and Kijung Shin
International Conference on Machine Learning (ICML) 2024 

[c24] NetEffect: Discovery and Exploitation of Generalized Network Effects
Meng-Chieh Lee, Shubhranshu Shekhar, Jaemin Yoo, and Christos Faloutsos
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2024 

[c23] HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs
Sunwoo Kim, Shinhwan Kang, Fanchen Bu, Soo Yong Lee, Jaemin Yoo, and Kijung Shin
International Conference on Learning Representations (ICLR) 2024 

[j6] Representative and Back-in-Time Sampling from Real-world Hypergraphs
Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, and Kijung Shin
Transactions on Knowledge Discovery from Data 

2023

[t2] Mining of Real-world Hypergraphs: Concepts, Patterns, and Generators (Tutorial)
Geon Lee, Jaemin Yoo, and Kijung Shin
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2023
The Web Conference (WWW) 2023

[c22] Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities (Short  Paper)
Leman Akoglu and Jaemin Yoo
IEEE International Conference on Big Data (BigData) 2023

[c21] DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection
Jaemin Yoo, Yue Zhao, Lingxiao Zhao, and Leman Akoglu
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2023

[c20] Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining
Jaemin Yoo*, Meng-Chieh Lee*, Shubhranshu Shekhar, and Christos Faloutsos
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2023

[c19] How Transitive Are Real-World Group Interactions? - Measurement and Reproduction
Sunwoo Kim, Fanchen Bu, Minyoung Choe, Jaemin Yoo, and Kijung Shin
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2023

[c18] Classification of Edge-dependent Labels of Nodes in Hypergraphs
Minyoung Choe, Sunwoo Kim, Jaemin Yoo, and Kijung Shin
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2023

[c17] Towards Deep Attention in Graph Neural Networks: Problems and Remedies
Soo Yong Lee, Fanchen Bu, Jaemin Yoo, and Kijung Shin
International Conference on Machine Learning (ICML) 2023

[j5] Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success
Jaemin Yoo, Tiancheng Zhao, and Leman Akoglu
Transactions on Machine Learning Research

[j4] Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators
Sunwoo Kim, Minyoung Choe, Jaemin Yoo, and Kijung Shin
Data Mining and Knowledge Discovery

2022

[t1] Mining of Real-world Hypergraphs: Concepts, Patterns, and Generators (Tutorial)
Geon Lee, Jaemin Yoo, and Kijung Shin
IEEE International Conference on Data Mining (ICDM) 2022
ACM International Conference on Information and Knowledge Management (CIKM) 2022

[c16] Accurate Stock Movement Prediction with Self-supervised Learning from Sparse Noisy Tweets
Yejun Soun*, Jaemin Yoo*, Minyong Cho, Jihyeong Jeon, and U Kang
IEEE International Conference on Big Data (BigData) 2022

[c15] Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators
Sunwoo Kim, Minyoung Choe, Jaemin Yoo, and Kijung Shin
IEEE International Conference on Data Mining (ICDM) 2022

[c14] Accurate Node Feature Estimation with Structured Variational Graph Autoencoder
Jaemin Yoo, Hyunsik Jeon, Jinhong Jung, and U Kang
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2022

[c13] Model-Agnostic Augmentation for Accurate Graph Classification
Jaemin Yoo, Sooyeon Shim, and U Kang
The Web Conference (WWW) 2022

[c12] MiDaS: Representative Sampling from Real-world Hypergraphs
Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, and Kijung Shin
The Web Conference (WWW) 2022

[c11] Transition Matrix Representation of Trees with Transposed Convolutions
Jaemin Yoo and Lee Sael
SIAM International Conference on Data Mining (SDM) 2022

[j3] Graph-based PU Learning for Binary and Multiclass Classification without Class Prior
Jaemin Yoo*, Junghun Kim*, Hoyoung Yoon*, Geonsoo Kim, Changwon Jang, and U Kang
Knowledge and Information Systems

[j2] Signed Random Walk Diffusion for Effective Representation Learning in Signed Graphs
Jinhong Jung, Jaemin Yoo, and U Kang
PLOS ONE

2021

[c10] Accurate Graph-Based PU Learning without Class Prior
Jaemin Yoo*, Junghun Kim*, Hoyoung Yoon*, Geonsoo Kim, Changwon Jang, and U Kang
IEEE International Conference on Data Mining (ICDM) 2021

[c9] Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts
Jaemin Yoo, Yejun Soun, Yong-chan Park, and U Kang
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2021

[c8] Gaussian Soft Decision Trees for Interpretable Feature-Based Classification
Jaemin Yoo and Lee Sael
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2021

[c7] Attention-Based Autoregression for Accurate and Efficient Multivariate Time Series Forecasting
Jaemin Yoo and U Kang
SIAM International Conference on Data Mining (SDM) 2021

2020

[c6] Sampling Subgraphs with Guaranteed Treewidth for Accurate and Efficient Graphical Inference
Jaemin Yoo, U Kang, Mauro Scanagatta, Giorgio Corani, and Marco Zaffalon
ACM International Conference on Web Search and Data Mining (WSDM) 2020

2019

[c5] Knowledge Extraction with No Observable Data
Jaemin Yoo, Minyong Cho, Taebum Kim, and U Kang
Conference on Neural Information Processing Systems (NeurIPS) 2019

[c4] EDiT: Interpreting Ensemble Models via Compact Soft Decision Trees
Jaemin Yoo and Lee Sael
IEEE International Conference on Data Mining (ICDM) 2019

[c3] Belief Propagation Network for Hard Inductive Semi-Supervised Learning
Jaemin Yoo, Hyunsik Jeon, and U Kang
International Joint Conference on Artificial Intelligence (IJCAI) 2019

2018 and Before

[c2] Fast and Scalable Distributed Loopy Belief Propagation on Real-World Graphs
Saehan Jo, Jaemin Yoo, and U Kang
ACM International Conference on Web Search and Data Mining (WSDM) 2018 

[c1] Supervised Belief Propagation: Scalable Supervised Inference on Attributed Networks
Jaemin Yoo, Saehan Jo, and U Kang
IEEE International Conference on Data Mining (ICDM) 2017

[j1] Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets
Mauro Scanagatta, Giorgio Corani, Marco Zaffalon, Jaemin Yoo, and U Kang
International Journal of Approximate Reasoning