| Continuous-time dynamic network embeddings GH Nguyen, JB Lee, RA Rossi, NK Ahmed, E Koh, S Kim Companion proceedings of the the web conference 2018, 969-976, 2018 | 574 | 2018 |
| Attention models in graphs: A survey JB Lee, RA Rossi, S Kim, NK Ahmed, E Koh ACM Transactions on Knowledge Discovery from Data (TKDD) 13 (6), 1-25, 2019 | 294 | 2019 |
| Temporal network representation learning JB Lee, G Nguyen, RA Rossi, NK Ahmed, E Koh, S Kim arXiv preprint arXiv:1904.06449, 2019 | 136 | 2019 |
| Higher-order network representation learning RA Rossi, NK Ahmed, E Koh Companion Proceedings of the The Web Conference 2018, 3-4, 2018 | 99 | 2018 |
| Graph convolutional networks with motif-based attention JB Lee, RA Rossi, X Kong, S Kim, E Koh, A Rao Proceedings of the 28th ACM international conference on information and …, 2019 | 95 | 2019 |
| combinFormation: Mixed-initiative composition of image and text surrogates promotes information discovery A Kerne, E Koh, SM Smith, A Webb, B Dworaczyk ACM Transactions on Information Systems (TOIS) 27 (1), 1-45, 2008 | 78 | 2008 |
| Dynamic network embeddings: From random walks to temporal random walks GH Nguyen, JB Lee, RA Rossi, NK Ahmed, E Koh, S Kim 2018 IEEE International Conference on Big Data (Big Data), 1085-1092, 2018 | 69 | 2018 |
| An experimental method for measuring the emergence of new ideas in information discovery A Kerne, SM Smith, E Koh, H Choi, R Graeber Intl. Journal of Human–Computer Interaction 24 (5), 460-477, 2008 | 56 | 2008 |
| combinFormation: A mixed-initiative system for representing collections as compositions of image and text surrogates A Kerne, E Koh, B Dworaczyk, JM Mistrot, H Choi, SM Smith, R Graeber, ... Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries, 11-20, 2006 | 53 | 2006 |
| Visualizing uncertainty and alternatives in event sequence predictions S Guo, F Du, S Malik, E Koh, S Kim, Z Liu, D Kim, H Zha, N Cao Proceedings of the 2019 CHI conference on human factors in computing systems …, 2019 | 52 | 2019 |
| A structural graph representation learning framework RA Rossi, NK Ahmed, E Koh, S Kim, A Rao, Y Abbasi-Yadkori Proceedings of the 13th international conference on web search and data …, 2020 | 46 | 2020 |
| Deepcompare: Visual and interactive comparison of deep learning model performance S Murugesan, S Malik, F Du, E Koh, TM Lai IEEE computer graphics and applications 39 (5), 47-59, 2019 | 46 | 2019 |
| Knowledge discovery from belief networks CR Ruiz, E Koh US Patent 9,691,032, 2017 | 44 | 2017 |
| Predicting online purchase conversion for retargeting J Yeo, S Kim, E Koh, S Hwang, N Lipka Proceedings of the Tenth ACM international conference on web search and data …, 2017 | 42 | 2017 |
| Higher-order graph convolutional networks JB Lee, RA Rossi, X Kong, S Kim, E Koh, A Rao arXiv preprint arXiv:1809.07697, 2018 | 41 | 2018 |
| Learning to recommend visualizations from data X Qian, RA Rossi, F Du, S Kim, E Koh, S Malik, TY Lee, J Chan Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 40 | 2021 |
| Graph convolutional networks with motif-based attention JBT Lee, R Rossi, S Kim, E Koh, A Rao US Patent 11,544,535, 2023 | 38 | 2023 |
| Promoting emergence in information discovery by representing collections with composition A Kerne, E Koh, S Smith, H Choi, R Graeber, A Webb Proceedings of the 6th ACM SIGCHI conference on Creativity & Cognition, 117-126, 2007 | 38 | 2007 |
| Figure captioning with reasoning and sequence-level training C Chen, R Zhang, E Koh, S Kim, S Cohen, T Yu, R Rossi, R Bunescu arXiv preprint arXiv:1906.02850, 2019 | 36 | 2019 |
| Representing collections as compositions to support distributed creative cognition and situated creative learning A Kerne, E Koh New Review of Hypermedia and Multimedia 13 (2), 135-162, 2007 | 35 | 2007 |
