Bear: Block elimination approach for random walk with restart on large graphs K Shin, J Jung, S Lee, U Kang Proceedings of the 2015 ACM SIGMOD international conference on management of …, 2015 | 97 | 2015 |
Learning to walk across time for interpretable temporal knowledge graph completion J Jung, J Jung, U Kang Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 80 | 2021 |
Personalized ranking in signed networks using signed random walk with restart J Jung, W Jin, L Sael, U Kang 2016 IEEE 16th international conference on data mining (ICDM), 973-978, 2016 | 74 | 2016 |
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 | 61 | 2017 |
Random walk with restart on large graphs using block elimination J Jung, K Shin, L Sael, U Kang ACM Transactions on Database Systems (TODS) 41 (2), 1-43, 2016 | 48 | 2016 |
A comparative study of matrix factorization and random walk with restart in recommender systems H Park, J Jung, U Kang 2017 IEEE International Conference on Big Data (Big Data), 756-765, 2017 | 42 | 2017 |
Tpa: Fast, scalable, and accurate method for approximate random walk with restart on billion scale graphs M Yoon, J Jung, U Kang 2018 IEEE 34th International Conference on Data Engineering (ICDE), 1132-1143, 2018 | 39 | 2018 |
Supervised and extended restart in random walks for ranking and link prediction in networks W Jin, J Jung, U Kang PloS one 14 (3), e0213857, 2019 | 36 | 2019 |
Random walk-based ranking in signed social networks: model and algorithms J Jung, W Jin, U Kang Knowledge and Information Systems 62 (2), 571-610, 2019 | 23 | 2019 |
Signed graph diffusion network J Jung, J Yoo, U Kang arXiv preprint arXiv:2012.14191, 2020 | 20 | 2020 |
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 | 14 | 2022 |
T-gap: Learning to walk across time for temporal knowledge graph completion J Jung, J Jung, U Kang arXiv preprint arXiv:2012.10595, 2020 | 13 | 2020 |
BalanSiNG: Fast and Scalable Generation of Realistic Signed Networks J Jung, HM Park, U Kang International Conference on Extending Database Technology (EDBT) 2020, 2020 | 13 | 2020 |
Zoom-svd: Fast and memory efficient method for extracting key patterns in an arbitrary time range JG Jang, D Choi, J Jung, U Kang Proceedings of the 27th ACM International Conference on Information and …, 2018 | 13 | 2018 |
Fast and accurate pseudoinverse with sparse matrix reordering and incremental approach J Jung, L Sael Machine Learning 109 (12), 2333-2347, 2020 | 11 | 2020 |
Compressing deep graph convolution network with multi-staged knowledge distillation J Kim, J Jung, U Kang Plos one 16 (8), e0256187, 2021 | 9 | 2021 |
Time-aware random walk diffusion to improve dynamic graph learning J Lee, J Jung Proceedings of the AAAI Conference on Artificial Intelligence 37 (7), 8473-8481, 2023 | 5 | 2023 |
Accurate relational reasoning in edge-labeled graphs by multi-labeled random walk with restart J Jung, W Jin, H Park, U Kang World Wide Web 24, 1369-1393, 2021 | 5 | 2021 |
Random walk with restart on hypergraphs: fast computation and an application to anomaly detection J Chun, G Lee, K Shin, J Jung Data Mining and Knowledge Discovery 38 (3), 1222-1257, 2024 | 3 | 2024 |
Signed random walk diffusion for effective representation learning in signed graphs J Jung, J Yoo, U Kang Plos one 17 (3), e0265001, 2022 | 3 | 2022 |