How do hyperedges overlap in real-world hypergraphs?-patterns, measures, and generators G Lee, M Choe, K Shin Proceedings of the web conference 2021, 3396-3407, 2021 | 54 | 2021 |
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 | 23 | 2022 |
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 | 12 | 2023 |
Hashnwalk: Hash and random walk based anomaly detection in hyperedge streams G Lee, M Choe, K Shin arXiv preprint arXiv:2204.13822, 2022 | 9 | 2022 |
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 | 5 | 2023 |
How transitive are real-world group interactions S Kim, F Bu, M Choe, J Yoo, K Shin Measurement and reproduction. arXiv preprint arXiv 2306, 2023 | 5 | 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 | 3 | 2023 |
Graphlets over Time: A New Lens for Temporal Network Analysis D Yoon, D Lee, M Choe, K Shin arXiv preprint arXiv:2301.00310, 2023 | 3 | 2023 |
Pretraining neural architecture search controllers with locality-based self-supervised learning K Choi, M Choe, H Lee arXiv preprint arXiv:2103.08157, 2021 | 2 | 2021 |
Temporal Graph Networks for Graph Anomaly Detection in Financial Networks Y Kim, Y Lee, M Choe, S Oh, Y Lee arXiv preprint arXiv:2404.00060, 2024 | | 2024 |
Representative and Back-In-Time Sampling from Real-World Hypergraphs M Choe, J Yoo, G Lee, W Baek, U Kang, K Shin ACM Transactions on Knowledge Discovery from Data, 2024 | | 2024 |