| Significance testing of word frequencies in corpora J Lijffijt, T Nevalainen, T Säily, P Papapetrou, K Puolamäki, H Mannila Digital Scholarship in the Humanities, 2014 | 147 | 2014 |
| Quantifying and minimizing risk of conflict in social networks X Chen, J Lijffijt, T De Bie Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 82 | 2018 |
| Correction to Stefan Th. Gries’ “Dispersions and adjusted frequencies in corpora” J Lijffijt, ST Gries International Journal of Corpus Linguistics 17 (1), 147-149, 2012 | 76 | 2012 |
| Conditional t-SNE: more informative t-SNE embeddings B Kang, D García García, J Lijffijt, R Santos-Rodríguez, T De Bie Machine Learning 110 (10), 2905-2940, 2021 | 73 | 2021 |
| Conditional Network Embeddings B Kang, J Lijffijt, T De Bie International Conference on Learning Representations, 2019 | 61 | 2019 |
| Large Language Models Reflect the Ideology of their Creators M Buyl, A Rogiers, S Noels, I Dominguez-Catena, E Heiter, R Romero, ... arXiv preprint arXiv:2410.18417, 2024 | 55 | 2024 |
| Opinion dynamics with backfire effect and biased assimilation X Chen, P Tsaparas, J Lijffijt, T De Bie PloS one 16 (9), e0256922, 2021 | 55 | 2021 |
| A challenge-based survey of e-recruitment recommendation systems Y Mashayekhi, N Li, B Kang, J Lijffijt, T De Bie ACM Computing Surveys 56 (10), 1-33, 2024 | 49 | 2024 |
| A statistical significance testing approach to mining the most informative set of patterns J Lijffijt, P Papapetrou, K Puolamäki Data Mining and Knowledge Discovery, 1-26, 2012 | 49 | 2012 |
| An Empirical Evaluation of Network Representation Learning Methods AC Mara, J Lijffijt, T De Bie Big Data, 2022 | 46* | 2022 |
| EvalNE: A framework for network embedding evaluation A Mara, J Lijffijt, T De Bie SoftwareX 17, 100997, 2022 | 39* | 2022 |
| Explanations for Network Embedding-based Link Predictions B Kang, J Lijffijt, T De Bie ECML PKDD International Workshop and Tutorial on eXplainable Knowledge …, 2021 | 38* | 2021 |
| CEECing the baseline: Lexical stability and significant change in a historical corpus J Lijffijt, T Säily, T Nevalainen Helsinki Corpus Festival, 2012 | 31 | 2012 |
| Size matters: Choosing the most informative set of window lengths for mining patterns in event sequences J Lijffijt, P Papapetrou, K Puolamäki Data Mining and Knowledge Discovery, 1-27, 2014 | 30* | 2014 |
| Analyzing word frequencies in large text corpora using inter-arrival times and bootstrapping J Lijffijt, P Papapetrou, K Puolamäki, H Mannila European Conference on Machine Learning and Principles and Practice of …, 2011 | 28 | 2011 |
| Subjectively interesting connecting trees and forests F Adriaens, J Lijffijt, T De Bie Data Mining and Knowledge Discovery 33 (4), 1088-1124, 2019 | 26* | 2019 |
| SICA: subjectively interesting component analysis B Kang, J Lijffijt, R Santos-Rodríguez, T De Bie Data Mining and Knowledge Discovery 32 (4), 949-987, 2018 | 26* | 2018 |
| Interactive visual data exploration with subjective feedback: An information-theoretic approach K Puolamäki, E Oikarinen, B Kang, J Lijffijt, T De Bie 2018 IEEE 34th International Conference on Data Engineering (ICDE), 1208-1211, 2018 | 23 | 2018 |
| A Constrained Randomization Approach to Interactive Visual Data Exploration with Subjective Feedback B Kang, K Puolamäki, J Lijffijt, T De Bie IEEE Transactions on Knowledge and Data Engineering, 2019 | 22* | 2019 |
| Benchmarking dynamic time warping for music retrieval J Lijffijt, P Papapetrou, J Hollmén, V Athitsos International Conference on PErvasive Technologies Related to Assistive …, 2010 | 20 | 2010 |
Tijl De BieFull Professor, Ghent UniversityVerified email at ugent.be
