| Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts J Yoo, Y Soun, Y Park, U Kang ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD), 2037-2045, 2021 | 121 | 2021 |
| Knowledge Extraction with No Observable Data J Yoo, M Cho, T Kim, U Kang Advances in Neural Information Processing Systems (NeurIPS), 2701-2710, 2019 | 120 | 2019 |
| Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets M Scanagatta, G Corani, M Zaffalon, J Yoo, U Kang International Journal of Approximate Reasoning 95, 152-166, 2018 | 42 | 2018 |
| Accurate stock movement prediction with self-supervised learning from sparse noisy tweets Y Soun, J Yoo, M Cho, J Jeon, U Kang 2022 IEEE International Conference on Big Data (Big Data), 1691-1700, 2022 | 41 | 2022 |
| Towards deep attention in graph neural networks: Problems and remedies SY Lee, F Bu, J Yoo, K Shin International Conference on Machine Learning, 18774-18795, 2023 | 30 | 2023 |
| Model-agnostic augmentation for accurate graph classification J Yoo, S Shim, U Kang Proceedings of the ACM Web Conference 2022, 1281-1291, 2022 | 28 | 2022 |
| Midas: Representative sampling from real-world hypergraphs M Choe, J Yoo, G Lee, W Baek, U Kang, K Shin Proceedings of the ACM Web Conference 2022, 1080-1092, 2022 | 26 | 2022 |
| Signed graph diffusion network J Jung, J Yoo, U Kang arXiv preprint arXiv:2012.14191, 2020 | 24 | 2020 |
| Attention-Based Autoregression for Accurate and Efficient Multivariate Time Series Forecasting J Yoo, U Kang SIAM International Conference on Data Mining (SDM), 531-539, 2021 | 22 | 2021 |
| Supervised Belief Propagation: Scalable Supervised Inference on Attributed Networks J Yoo, S Jo, U Kang IEEE International Conference on Data Mining (ICDM), 595-604, 2017 | 21 | 2017 |
| Belief Propagation Network for Hard Inductive Semi-Supervised Learning J Yoo, H Jeon, U Kang International Joint Conference on Artificial Intelligence (IJCAI), 4178-4184, 2019 | 19 | 2019 |
| Fast and scalable distributed loopy belief propagation on real-world graphs S Jo, J Yoo, U Kang ACM International Conference on Web Search and Data Mining (WSDM), 297-305, 2018 | 18 | 2018 |
| Accurate node feature estimation with structured variational graph autoencoder J Yoo, H Jeon, J Jung, U Kang Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 17 | 2022 |
| Accurate Graph-Based PU Learning without Class Prior J Yoo, J Kim, H Yoon, G Kim, C Jang, U Kang IEEE International Conference on Data Mining (ICDM), 827-836, 2021 | 16 | 2021 |
| Reciprocity in directed hypergraphs: measures, findings, and generators S Kim, M Choe, J Yoo, K Shin Data Mining and Knowledge Discovery 37 (6), 2330-2388, 2023 | 15 | 2023 |
| Sampling Subgraphs with Guaranteed Treewidth for Accurate and Efficient Graphical Inference J Yoo, U Kang, M Scanagatta, G Corani, M Zaffalon ACM International Conference on Web Search and Data Mining (WSDM), 708-716, 2020 | 14 | 2020 |
| Less is more: Slimg for accurate, robust, and interpretable graph mining J Yoo, MC Lee, S Shekhar, C Faloutsos Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 9* | 2023 |
| Classification of edge-dependent labels of nodes in hypergraphs M Choe, S Kim, J Yoo, K Shin Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 9 | 2023 |
| How Transitive Are Real-World Group Interactions?-Measurement and Reproduction S Kim, F Bu, M Choe, J Yoo, K Shin Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 9 | 2023 |
| Mining of real-world hypergraphs: Patterns, tools, and generators G Lee, J Yoo, K Shin Proceedings of the 31st ACM International Conference on Information …, 2022 | 9 | 2022 |
U KangProfessor of Computer Science and Engineering, Seoul National UniversityVerified email at snu.ac.kr
