| A block model for node popularity in networks with community structure S Sengupta, Y Chen Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2018 | 71 | 2018 |
| Spectral clustering in heterogeneous networks S Sengupta, Y Chen Statistica Sinica, 1081-1106, 2015 | 54 | 2015 |
| A subsampled double bootstrap for massive data S Sengupta, S Volgushev, X Shao Journal of the American Statistical Association 111 (515), 1222-1232, 2016 | 48 | 2016 |
| Toward epidemic thresholds on temporal networks: a review and open questions J Leitch, KA Alexander, S Sengupta Applied Network Science 4, 1-21, 2019 | 40 | 2019 |
| Online social deception and its countermeasures: A survey Z Guo, JH Cho, R Chen, S Sengupta, M Hong, T Mitra Ieee Access 9, 1770-1806, 2020 | 37 | 2020 |
| Performance evaluation of social network anomaly detection using a moving window–based scan method MJ Zhao, AR Driscoll, S Sengupta, RD Fricker Jr, DJ Spitzner, ... Quality and Reliability Engineering International 34 (8), 1699-1716, 2018 | 34 | 2018 |
| The effect of temporal aggregation level in social network monitoring MJ Zhao, AR Driscoll, S Sengupta, NT Stevens, RD Fricker Jr, ... PloS one 13 (12), e0209075, 2018 | 26 | 2018 |
| Using artificial neural networks to predict pH, ammonia, and volatile fatty acid concentrations in the rumen MM Li, S Sengupta, MD Hanigan Journal of dairy science 102 (10), 8850-8861, 2019 | 25 | 2019 |
| Statistical challenges in online controlled experiments: A review of a/b testing methodology N Larsen, J Stallrich, S Sengupta, A Deng, R Kohavi, NT Stevens The American Statistician 78 (2), 135-149, 2024 | 18 | 2024 |
| The value of summary statistics for anomaly detection in temporally evolving networks: A performance evaluation study L Kodali, S Sengupta, L House, WH Woodall Applied Stochastic Models in Business and Industry 36 (6), 980-1013, 2020 | 14 | 2020 |
| Statistical evaluation of spectral methods for anomaly detection in static networks T Komolafe, AV Quevedo, S Sengupta, WH Woodall Network Science 7 (3), 319-352, 2019 | 13 | 2019 |
| Discussion of “Statistical methods for network surveillance” S Sengupta, WH Woodall Applied Stochastic Models in Business and Industry 34 (4), 446-448, 2018 | 13 | 2018 |
| Safer: Social capital-based friend recommendation to defend against phishing attacks Z Guo, JH Cho, R Chen, S Sengupta, M Hong, T Mitra Proceedings of the International AAAI Conference on Web and Social Media 16 …, 2022 | 9 | 2022 |
| Anomaly detection in static networks using egonets S Sengupta arXiv preprint arXiv:1807.08925, 2018 | 9 | 2018 |
| The dependent random weighting S Sengupta, X Shao, Y Wang Journal of Time Series Analysis 36 (3), 315-326, 2015 | 9 | 2015 |
| Core-periphery structure in networks: A statistical exposition E Yanchenko, S Sengupta Statistic Surveys 17, 42-74, 2023 | 8 | 2023 |
| Scalable estimation of epidemic thresholds via node sampling A Dasgupta, S Sengupta Sankhya A 84 (1), 321-344, 2022 | 6 | 2022 |
| Research in network monitoring: Connections with SPM and new directions NT Stevens, JD Wilson, AR Driscoll, I McCulloh, G Michailidis, C Paris, ... Quality Engineering 33 (4), 736-748, 2021 | 6 | 2021 |
| Foundations of network monitoring: Definitions and applications NT Stevens, JD Wilson, AR Driscoll, I McCulloh, G Michailidis, C Paris, ... Quality Engineering 33 (4), 719-730, 2021 | 6 | 2021 |
| A bootstrap-based inference framework for testing similarity of paired networks S Bhadra, K Chakraborty, S Sengupta, S Lahiri arXiv preprint arXiv:1911.06869, 2019 | 6 | 2019 |
