Stebėti
Santiago Segarra
Santiago Segarra
Associate Professor, Electrical and Computer Engineering, Rice University
Patvirtintas el. paštas rice.edu - Pagrindinis puslapis
Pavadinimas
Cituota
Cituota
Metai
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
4002019
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
3452017
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
3212015
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
2832017
Stationary graph processes and spectral estimation
AG Marques, S Segarra, G Leus, A Ribeiro
IEEE Transactions on Signal Processing 65 (22), 5911-5926, 2017
2602017
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
1642021
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
1552021
Stability and continuity of centrality measures in weighted graphs
S Segarra, A Ribeiro
IEEE Transactions on Signal Processing 64 (3), 543-555, 2015
1422015
Principled simplicial neural networks for trajectory prediction
TM Roddenberry, N Glaze, S Segarra
International Conference on Machine Learning, 9020-9029, 2021
1202021
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
1132016
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
1052018
Authorship attribution through function word adjacency networks
S Segarra, M Eisen, A Ribeiro
IEEE Transactions on Signal Processing 63 (20), 5464-5478, 2015
1042015
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
1002019
Blind identification of graph filters
S Segarra, G Mateos, AG Marques, A Ribeiro
IEEE Transactions on Signal Processing 65 (5), 1146-1159, 2016
952016
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
902018
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
812024
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
702021
Axiomatic construction of hierarchical clustering in asymmetric networks
G Carlsson, F Mémoli, A Ribeiro, S Segarra
2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013
632013
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
622016
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
592020
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Straipsniai 1–20