| Connecting the dots: Identifying network structure via graph signal processing G Mateos, S Segarra, AG Marques, A Ribeiro IEEE Signal Processing Magazine 36 (3), 16-43, 2019 | 466 | 2019 |
| Network topology inference from spectral templates S Segarra, AG Marques, G Mateos, A Ribeiro IEEE Transactions on Signal and Information Processing over Networks 3 (3 …, 2017 | 370 | 2017 |
| Sampling of graph signals with successive local aggregations AG Marques, S Segarra, G Leus, A Ribeiro IEEE Transactions on Signal Processing 64 (7), 1832-1843, 2015 | 337 | 2015 |
| Optimal graph-filter design and applications to distributed linear network operators S Segarra, AG Marques, A Ribeiro IEEE Transactions on Signal Processing 65 (15), 4117-4131, 2017 | 298 | 2017 |
| Stationary graph processes and spectral estimation AG Marques, S Segarra, G Leus, A Ribeiro IEEE Transactions on Signal Processing 65 (22), 5911-5926, 2017 | 285 | 2017 |
| Signal processing on higher-order networks: Livin’on the edge... and beyond MT Schaub, Y Zhu, JB Seby, TM Roddenberry, S Segarra Signal Processing 187, 108149, 2021 | 202 | 2021 |
| Unfolding WMMSE using graph neural networks for efficient power allocation A Chowdhury, G Verma, C Rao, A Swami, S Segarra IEEE Transactions on Wireless Communications 20 (9), 6004-6017, 2021 | 177 | 2021 |
| Graph filters for signal processing and machine learning on graphs E Isufi, F Gama, DI Shuman, S Segarra IEEE Transactions on Signal Processing 72, 4745-4781, 2024 | 169 | 2024 |
| Stability and continuity of centrality measures in weighted graphs S Segarra, A Ribeiro IEEE Transactions on Signal Processing 64 (3), 543-555, 2015 | 162 | 2015 |
| Principled simplicial neural networks for trajectory prediction TM Roddenberry, N Glaze, S Segarra International Conference on Machine Learning, 9020-9029, 2021 | 138 | 2021 |
| Centrality measures for graphons: Accounting for uncertainty in networks M Avella-Medina, F Parise, M Schaub, S Segarra IEEE Transactions on Network Science and Engineering, 2018 | 128 | 2018 |
| Reconstruction of graph signals through percolation from seeding nodes S Segarra, AG Marques, G Leus, A Ribeiro IEEE Transactions on Signal Processing 64 (16), 4363-4378, 2016 | 115 | 2016 |
| Authorship attribution through function word adjacency networks S Segarra, M Eisen, A Ribeiro IEEE Transactions on Signal Processing 63 (20), 5464-5478, 2015 | 111 | 2015 |
| Flow smoothing and denoising: Graph signal processing in the edge-space MT Schaub, S Segarra 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP …, 2018 | 106 | 2018 |
| Graph-based semi-supervised & active learning for edge flows J Jia, MT Schaub, S Segarra, AR Benson Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019 | 105 | 2019 |
| Blind identification of graph filters S Segarra, G Mateos, AG Marques, A Ribeiro IEEE Transactions on Signal Processing 65 (5), 1146-1159, 2016 | 104 | 2016 |
| Distributed scheduling using graph neural networks Z Zhao, G Verma, C Rao, A Swami, S Segarra ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 81 | 2021 |
| Signal processing on directed graphs: The role of edge directionality when processing and learning from network data AG Marques, S Segarra, G Mateos IEEE Signal Processing Magazine 37 (6), 99-116, 2020 | 79 | 2020 |
| HodgeNet: Graph neural networks for edge data TM Roddenberry, S Segarra 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 220-224, 2019 | 70 | 2019 |
| Attributing the authorship of the Henry VI plays by word adjacency S Segarra, M Eisen, G Egan, A Ribeiro Shakespeare Quarterly 67 (2), 232-256, 2016 | 68 | 2016 |
Antonio G. MarquesProfessor of ECE, King Juan Carlos University (Universidad Rey Juan Carlos), MadridVerified email at urjc.es
