| Comparing the accuracy of several network-based COVID-19 prediction algorithms MA Achterberg, B Prasse, L Ma, S Trajanovski, M Kitsak, P Van Mieghem International journal of forecasting 38 (2), 489-504, 2022 | 69 | 2022 |
| Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei B Prasse, MA Achterberg, L Ma, P Van Mieghem Applied Network Science 5, 1-11, 2020 | 69 | 2020 |
| Efficient reconstruction of heterogeneous networks from time series via compressed sensing L Ma, X Han, Z Shen, WX Wang, Z Di PloS one 10 (11), e0142837, 2015 | 24 | 2015 |
| Spreading to localized targets in complex networks Y Sun, L Ma, A Zeng, WX Wang Scientific reports 6 (1), 38865, 2016 | 19 | 2016 |
| Inferring network properties based on the epidemic prevalence L Ma, Q Liu, P Van Mieghem Applied Network Science 4, 1-13, 2019 | 10 | 2019 |
| Markov chains and hitting times for error accumulation in quantum circuits L Ma, J Sanders arXiv preprint arXiv:1909.04432, 2019 | 1 | 2019 |
| Reporting delays: a widely neglected impact factor in COVID-19 forecasts L Ma, P Van Mieghem, M Kitsak arXiv preprint arXiv:2304.11863, 2023 | | 2023 |
| Two-population SIR model and strategies to reduce mortality in pandemics L Ma, M Kitsak, P Van Mieghem Complex Networks & Their Applications X: Volume 2, Proceedings of the Tenth …, 2022 | | 2022 |
| Characterizing the Divergence Between Two Different Models for Fitting and Forecasting the COVID-19 Pandemic T Gan, L Ma EasyChair, 2021 | | 2021 |
| Spreading processes in complex networks and systems. L Ma Delft University of Technology, Netherlands, 2021 | | 2021 |
| Markov chains and hitting times for error accumulation in quantum circuits L Ma, J Sanders Performance Evaluation Methodologies and Tools: 14th EAI International …, 2021 | | 2021 |
