| Personalized graph summarization: formulation, scalable algorithms, and applications S Kang, K Lee, K Shin 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2319-2332, 2022 | 8 | 2022 |
| Weather4cast at neurips 2022: Super-resolution rain movie prediction under spatio-temporal shifts A Gruca, F Serva, L Lliso, P Rípodas, X Calbet, P Herruzo, J Pihrt, ... NeurIPS 2022 Competition Track, 292-313, 2023 | 5 | 2023 |
| Begin: Extensive benchmark scenarios and an easy-to-use framework for graph continual learning J Ko, S Kang, T Kwon, H Moon, K Shin arXiv preprint arXiv:2211.14568, 2022 | 4 | 2022 |
| Hypeboy: Generative self-supervised representation learning on hypergraphs S Kim, S Kang, F Bu, SY Lee, J Yoo, K Shin arXiv preprint arXiv:2404.00638, 2024 | 2 | 2024 |
| Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition T Kim, S Kang, H Shin, D Yoon, S Eom, K Shin, SY Yun arXiv preprint arXiv:2212.02059, 2022 | 2 | 2022 |
| Are edge weights in summary graphs useful?-a comparative study S Kang, K Lee, K Shin Pacific-Asia Conference on Knowledge Discovery and Data Mining, 54-67, 2022 | 2 | 2022 |
| Interplay between topology and edge weights in real-world graphs: concepts, patterns, and an algorithm F Bu, S Kang, K Shin Data Mining and Knowledge Discovery 37 (6), 2139-2191, 2023 | 1 | 2023 |
| On Making Graph Continual Learning Easy, Fool-Proof, and Extensive: a Benchmark Framework and Scenarios J Ko, S Kang, K Shin | | 2022 |
Kijung ShinAssociate Professor, KAISTBestätigte E-Mail-Adresse bei kaist.ac.kr
