Supervised Belief Propagation: Scalable Supervised Inference on Attributed Networks. - Jaemin Yoo; Saehan Jo; U Kang (ICDM, 2017)

Fast and Scalable Distributed Loopy Belief Propagation on Real-World Graphs. - Saehan Jo; Jaemin Yoo; U Kang (WSDM, 2018)

Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets. - Mauro Scanagatta; Giorgio Corani; Marco Zaffalon; Jaemin Yoo; U Kang (Int. J. Approx. Reason., 2018)

EDiT: Interpreting Ensemble Models via Compact Soft Decision Trees. - Jaemin Yoo; Lee Sael (ICDM, 2019)

Belief Propagation Network for Hard Inductive Semi-Supervised Learning. - Jaemin Yoo; Hyunsik Jeon; U Kang (IJCAI, 2019)

Knowledge Extraction with No Observable Data. - Jaemin Yoo; Minyong Cho; Taebum Kim; U Kang (NeurIPS, 2019)

Sampling Subgraphs with Guaranteed Treewidth for Accurate and Efficient Graphical Inference. - Jaemin Yoo; U Kang; Mauro Scanagatta; Giorgio Corani; Marco Zaffalon (WSDM, 2020)

Signed Graph Diffusion Network. - Jinhong Jung; Jaemin Yoo; U Kang (CoRR, 2020)

Accurate Graph-Based PU Learning without Class Prior. - Jaemin Yoo; Junghun Kim; Hoyoung Yoon; Geonsoo Kim; Changwon Jang; U Kang (ICDM, 2021)

Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts. - Jaemin Yoo; Yejun Soun; Yong-chan Park; U Kang (KDD, 2021)

Gaussian Soft Decision Trees for Interpretable Feature-Based Classification. - Jaemin Yoo; Lee Sael (PAKDD (2), 2021)

Attention-Based Autoregression for Accurate and Efficient Multivariate Time Series Forecasting. - Jaemin Yoo; U Kang (SDM, 2021)

Accurate Stock Movement Prediction with Self-supervised Learning from Sparse Noisy Tweets. - Yejun Soun; Jaemin Yoo; Minyong Cho; Jihyeong Jeon; U Kang (IEEE Big Data, 2022)

Mining of Real-world Hypergraphs: Patterns, Tools, and Generators. - Geon Lee; Jaemin Yoo; Kijung Shin (CIKM, 2022)

Accurate Node Feature Estimation with Structured Variational Graph Autoencoder. - Jaemin Yoo; Hyunsik Jeon; Jinhong Jung; U Kang (KDD, 2022)

Transition Matrix Representation of Trees with Transposed Convolutions. - Jaemin Yoo; Lee Sael (SDM, 2022)

MiDaS: Representative Sampling from Real-world Hypergraphs. - Minyoung Choe; Jaemin Yoo; Geon Lee; Woonsung Baek; U Kang; Kijung Shin (WWW, 2022)

Model-Agnostic Augmentation for Accurate Graph Classification. - Jaemin Yoo; Sooyeon Shim; U Kang (WWW, 2022)

Understanding the Effect of Data Augmentation in Self-supervised Anomaly Detection. - Jaemin Yoo; Tiancheng Zhao; Leman Akoglu (CoRR, 2022)

SlenderGNN: Accurate, Robust, and Interpretable GNN, and the Reasons for its Success. - Jaemin Yoo; Meng-Chieh Lee; Shubhranshu Shekhar; Christos Faloutsos (CoRR, 2022)

Graph-based PU learning for binary and multiclass classification without class prior. - Jaemin Yoo; Junghun Kim; Hoyoung Yoon; Geonsoo Kim; Changwon Jang; U Kang (Knowl. Inf. Syst., 2022)

Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities. - Leman Akoglu; Jaemin Yoo (IEEE Big Data, 2023)

Towards Deep Attention in Graph Neural Networks: Problems and Remedies. - Soo Yong Lee; Fanchen Bu; Jaemin Yoo; Kijung Shin (ICML, 2023)

Classification of Edge-dependent Labels of Nodes in Hypergraphs. - Minyoung Choe; Sunwoo Kim 006; Jaemin Yoo; Kijung Shin (KDD, 2023)

How Transitive Are Real-World Group Interactions? - Measurement and Reproduction. - Sunwoo Kim 006; Fanchen Bu; Minyoung Choe; Jaemin Yoo; Kijung Shin (KDD, 2023)

Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining. - Jaemin Yoo; Meng-Chieh Lee; Shubhranshu Shekhar; Christos Faloutsos (KDD, 2023)

DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection. - Jaemin Yoo; Yue Zhao 016; Lingxiao Zhao; Leman Akoglu (ECML/PKDD (1), 2023)

Tutorials at The Web Conference 2023. - Valeria Fionda; Olaf Hartig; Reyhaneh Abdolazimi; Sihem Amer-Yahia; Hongzhi Chen; Xiao Chen 016; Peng Cui 001; Jeffrey Dalton 001; Xin Luna Dong; Lisette Espín-Noboa; Wenqi Fan; Manuela Fritz; et al. (WWW (Companion Volume), 2023)

UltraProp: Principled and Explainable Propagation on Large Graphs. - Meng-Chieh Lee; Shubhranshu Shekhar; Jaemin Yoo; Christos Faloutsos (CoRR, 2023)

End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection. - Jaemin Yoo; Lingxiao Zhao; Leman Akoglu (CoRR, 2023)

Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success. - Jaemin Yoo; Tiancheng Zhao; Leman Akoglu (Trans. Mach. Learn. Res., 2023)

Reciprocity in directed hypergraphs: measures, findings, and generators. - Sunwoo Kim 006; Minyoung Choe; Jaemin Yoo; Kijung Shin (Data Min. Knowl. Discov., 2023)

HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs. - Sunwoo Kim 006; Shinhwan Kang; Fanchen Bu; Soo Yong Lee; Jaemin Yoo; Kijung Shin (ICLR, 2024)

Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective. - Soo Yong Lee; Sunwoo Kim 006; Fanchen Bu; Jaemin Yoo; Jiliang Tang; Kijung Shin (ICML, 2024)

Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy. - Sunwoo Kim 006; Soo Yong Lee; Fanchen Bu; Shinhwan Kang; Kyungho Kim; Jaemin Yoo; Kijung Shin (NeurIPS, 2024)

NETEFFECT: Discovery and Exploitation of Generalized Network Effects. - Meng-Chieh Lee; Shubhranshu Shekhar; Jaemin Yoo; Christos Faloutsos (PAKDD (1), 2024)

End-To-End Self-tuning Self-supervised Time Series Anomaly Detection. - Boje Deforce; Meng-Chieh Lee; Bart Baesens; Estefanía Serral Asensio; Jaemin Yoo; Leman Akoglu (CoRR, 2024)

Political-LLM: Large Language Models in Political Science. - Lincan Li; Jiaqi Li; Catherine Chen 003; Fred Gui; Hongjia Yang; Chenxiao Yu; Zhengguang Wang; Jianing Cai; Junlong Aaron Zhou; Bolin Shen; Alex Qian; Weixin Chen; et al. (CoRR, 2024)

Representative and Back-In-Time Sampling from Real-world Hypergraphs. - Minyoung Choe; Jaemin Yoo; Geon Lee; Woonsung Baek; U Kang; Kijung Shin (ACM Trans. Knowl. Discov. Data, 2024)

End-To-End Self-Tuning Self-Supervised Time Series Anomaly Detection. - Boje Deforce; Meng-Chieh Lee; Bart Baesens; Estefanía Serrai Asensio; Jaemin Yoo; Leman Akoglu (SDM, 2025)

Criteria-Aware Graph Filtering: Extremely Fast Yet Accurate Multi-Criteria Recommendation. - Jin-Duk Park; Jaemin Yoo; Won-Yong Shin (WWW, 2025)

Emergence of psychopathological computations in large language models. - Soo Yong Lee; Hyunjin Hwang; Taekwan Kim; Yuyeong Kim; Kyuri Park; Jaemin Yoo; Denny Borsboom; Kijung Shin (CoRR, 2025)

'Hello, World!': Making GNNs Talk with LLMs. - Sunwoo Kim; Soo Yong Lee; Jaemin Yoo; Kijung Shin (CoRR, 2025)

Aggregation Buffer: Revisiting DropEdge with a New Parameter Block. - Dooho Lee; Myeong Kong; Sagad Hamid; Cheonwoo Lee; Jaemin Yoo (CoRR, 2025)