| Estimating node importance in knowledge graphs using graph neural networks N Park, A Kan, XL Dong, T Zhao, C Faloutsos Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 161 | 2019 |
| Scalable tucker factorization for sparse tensors-algorithms and discoveries S Oh, N Park, S Lee, U Kang 2018 IEEE 34th International Conference on Data Engineering (ICDE), 1120-1131, 2018 | 76 | 2018 |
| EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs N Park, F Liu, P Mehta, D Cristofor, C Faloutsos, Y Dong Proceedings of the Fifteenth ACM International Conference on Web Search and …, 2022 | 73 | 2022 |
| Bepi: Fast and memory-efficient method for billion-scale random walk with restart J Jung, N Park, S Lee, U Kang Proceedings of the 2017 ACM International Conference on Management of Data …, 2017 | 63 | 2017 |
| Bigtensor: Mining billion-scale tensor made easy N Park, B Jeon, J Lee, U Kang Proceedings of the 25th ACM International on Conference on Information and …, 2016 | 50 | 2016 |
| CGC: Contrastive Graph Clustering for Community Detection and Tracking N Park, R Rossi, E Koh, IA Burhanuddin, S Kim, F Du, N Ahmed, ... Proceedings of the ACM Web Conference 2022, 1115-1126, 2022 | 39 | 2022 |
| Predicting acute kidney injury in cancer patients using heterogeneous and irregular data N Park, E Kang, M Park, H Lee, HG Kang, HJ Yoon, U Kang PloS one 13 (7), e0199839, 2018 | 38 | 2018 |
| High-performance tucker factorization on heterogeneous platforms S Oh, N Park, JG Jang, L Sael, U Kang IEEE Transactions on Parallel and Distributed Systems 30 (10), 2237-2248, 2019 | 31 | 2019 |
| Acute kidney injury predicts all‐cause mortality in patients with cancer E Kang, M Park, PG Park, N Park, Y Jung, U Kang, HG Kang, DK Kim, ... Cancer medicine 8 (6), 2740-2750, 2019 | 28 | 2019 |
| MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals N Park, A Kan, XL Dong, T Zhao, C Faloutsos Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 27 | 2020 |
| Fast and scalable distributed boolean tensor factorization N Park, S Oh, U Kang 2017 IEEE 33rd International Conference on Data Engineering (ICDE), 1071-1082, 2017 | 25 | 2017 |
| INFOSHIELD: Generalizable Information-Theoretic Human-Trafficking Detection MC Lee, C Vajiac, A Kulshrestha, S Levy, N Park, C Jones, R Rabbany, ... 2021 IEEE 37th International Conference on Data Engineering (ICDE), 1116–1127, 2021 | 20 | 2021 |
| Fast and scalable method for distributed boolean tensor factorization N Park, S Oh, U Kang The VLDB Journal 28 (4), 549-574, 2019 | 20 | 2019 |
| Evolving the best known approximation to the q function DN Phong, NX Hoai, RI McKay, C Siriteanu, NQ Uy, N Park Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012 | 17 | 2012 |
| Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning B Jeon, N Park, S Bang arXiv preprint arXiv:2002.01598, 2020 | 12 | 2020 |
| Dropout Prediction over Weeks in MOOCs by Learning Representations of Clicks and Videos B Jeon, N Park arXiv preprint arXiv:2002.01955, 2020 | 12 | 2020 |
| Partition aware connected component computation in distributed systems HM Park, N Park, SH Myaeng, U Kang 2016 IEEE 16th International Conference on Data Mining (ICDM), 420-429, 2016 | 12 | 2016 |
| Fairness-Aware Graph Neural Networks: A Survey A Chen, RA Rossi, N Park, P Trivedi, Y Wang, T Yu, S Kim, F Dernoncourt, ... ACM Transactions on Knowledge Discovery from Data, 2024 | 10 | 2024 |
| MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning N Park, RA Rossi, N Ahmed, C Faloutsos International Conference on Learning Representations, 2023 | 8* | 2023 |
| PACC: Large scale connected component computation on Hadoop and Spark HM Park, N Park, SH Myaeng, U Kang PLOS ONE 15 (3), e0229936, 2020 | 8 | 2020 |
