Publications

In the publications marked with *, authors are ordered alphabetically.
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Journal Papers

  1. T. M. Roddenberry and S. Segarra, Limits of Dense Simplicial Complexes, J. Mach. Learn. Res., vol. 24, no. 225, pp. 1-42, 2023. Arxiv Preprint
  2. S. Rey, T. M. Roddenberry, S. Segarra and A. G. Marques, Enhanced Graph-Learning Schemes Driven by Similar Distributions of Motifs, in IEEE Trans. Signal Process., vol. 71, pp. 3014-3027, 2023. Arxiv Preprint
  3. Y. Zhu, A. Swami, and S. Segarra, Free Energy Node Embedding via Generalized Skip-gram with Negative Sampling, IEEE Trans. Knowledge and Data Eng., vol. 35, no. 8, pp. 8024-8036, 2023. Arxiv Preprint
  4. G. Egan, M. Eisen, A. Ribeiro, and S. Segarra, “I would I had that corporal soundness”: Pervez Rizvi’s Analysis of the Word Adjacency Network Method of Authorship Attribution, Digital Scholarship in the Humanities, vol. 38, no. 4, pp. 1494-1507, 2023.
  5. L O’Bryan, T. Oxendahl, X. Chen, D. McDuff, S. Segarra, M. Wettergreen, M. E. Beier, and A. Sabharwal, Objective Communication Patterns Associated With Team Member Effectiveness in Real-World Virtual Teams, Human Factors, 2022.
  6. N. Zilberstein, C. Dick, R. Doost-Mohammady, A. Sabharwal, and S. Segarra, Annealed Langevin Dynamics for Massive MIMO Detection, IEEE Trans. Wireless Comm., vol. 22, no. 6, pp. 3762-3776, 2023. Arxiv Preprint
  7. Z. Zhao, G. Verma, C. Rao, A. Swami, and S. Segarra, Link Scheduling using Graph Neural Networks, IEEE Trans. Wireless Comm., vol. 22, no. 6, pp. 3997-4012, 2023. Arxiv Preprint
  8. Y. Zhu and S. Segarra, Hypergraphs with Edge-Dependent Vertex Weights: p-Laplacians and Spectral Clustering, Frontiers in Big Data, vol. 6, pp. 1020173, 2023. Arxiv Preprint
  9. B. Li, G. Verma, and S. Segarra, Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks, IEEE Trans. Wireless Comm., vol. 22, no. 2, pp. 1359-1373, 2023. Arxiv Preprint
  10. M. Navarro and S. Segarra, Joint Network Topology Inference Via a Shared Graphon Model, IEEE Trans. Signal Process., vol. 70, pp. 5549-5563, 2022. Arxiv Preprint
  11. T. M. Roddenberry, F. Gama, R. G. Baraniuk, and S. Segarra, On Local Distributions in Graph Signal Processing, IEEE Trans. Signal Process., vol. 70, pp. 5564-5577, 2022. Arxiv Preprint
  12. S. Rey, S. Segarra, R. Heckel, and A. G. Marques, Untrained Graph Neural Networks for Denoising, IEEE Trans. Signal Process., vol. 70, pp. 5708-5723, 2022. Arxiv Preprint
  13. Y. Zhu and S. Segarra, Hypergraph Cuts with Edge-Dependent Vertex Weights, Applied Network Science, vol. 23, art. 45, 2022. Arxiv Preprint
  14. A. Balaji, B. Kille, A. D. Kappell, G. D. Godbold, M. Diep, R. A. L. Elworth, Z. Qian, D. Albin, D. J. Nasko, N. Shah, M. Pop, S. Segarra, K. L. Ternus, and T. J. Treangen, SeqScreen: Accurate and Sensitive Functional Screening of Pathogenic Sequences via Ensemble Learning, Genome Biology, vol. 23, art. 133, 2022. BioRxiv Preprint
  15. M. Navarro, Y. Wang, A. G. Marques, C. Uhler, and S. Segarra, Joint Inference of Multiple Graphs from Matrix Polynomials, J. Mach. Learn. Res., vol. 23, no. 76, pp 1-35, 2022. Arxiv Preprint
  16. A. Balaji, N. Sapoval, C. Seto, R. A. L. Elworth, Y. Fu, M. G. Nute, T. Savidge, S. Segarra, and T. J. Treangen, KOMB: K-core Based de Novo Characterization of Copy Number Variation in Microbiomes, Comp. and Struc. Biotech. J., vol. 20, pp. 3208-3222, 2022. BioRxiv Preprint
  17. P. Brown, M. Eisen, S. Segarra, A. Ribeiro, and G. Egan, How the Word Adjacency Network (WAN) algorithm works, Digital Scholarship in the Humanities, vol. 37, no. 2, pp. 321-335, 2022.
  18. G. Carlsson, F. Memoli, and S. Segarra, Robust Hierarchical Clustering for Directed Networks: An Axiomatic Approach, SIAM J. Applied Algebra and Geometry (SIAGA), vol. 5, no. 4, pp. 675-700, 2021. Arxiv Preprint
  19. T. M. Roddenberry and S. Segarra, Blind Inference of Eigenvector Centrality Rankings, IEEE Trans. Signal Process., vol. 69, pp. 3935-3946, 2021. Arxiv Preprint
  20. A. Chowdhury, G. Verma, C. Rao, A. Swami, and S. Segarra, Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation, IEEE Trans. Wireless Comm., vol. 20, no. 10, pp. 6004-6017, Sept. 2021. Arxiv Preprint
  21. D. Ramírez, A. G. Marques, and S. Segarra, Graph-signal Reconstruction and Blind Deconvolution for Structured Inputs, Signal Processing, vol. 188, pp. 108180, 2021. Arxiv Preprint
  22. S. Segarra, M. Eisen, G. Egan, and A. Ribeiro, A Response to Rosalind Barber’s Critique of the Word Adjacency Method for Authorship Attribution, ANQ: A Quarterly Journal of Short Articles, Notes and Reviews, vol. 34, no. 4, pp. 291-296, 2021.
  23. M. T. Schaub, Y. Zhu, J. B. Seby, T. M. Roddenberry, and S. Segarra, Signal Processing on Higher-Order Networks: Livin' on the Edge... and Beyond, Signal Processing, vol. 187, pp. 108149, 2021. Arxiv Preprint
  24. G. Leus, S. Segarra, A. Ribeiro, and A. G. Marques, The Dual Graph Shift Operator: Identifying the Support of the Frequency Domain, Journal of Fourier Analysis and Applications, vol. 27, art. 49, May 2021. Arxiv Preprint 
  25. R. Shafipour, S. Segarra, A. G. Marques, and G. Mateos, Identifying the Topology of Undirected Networks from Diffused Non-stationary Graph Signals, IEEE Open J. Signal Process., vol. 2, pp. 171-189, March 2021. Arxiv Preprint
  26. B. Lamichhane, Y. Kim, S. Segarra, G. Zhang, S. Lhatoo, J. Hampson, and X. Jiang, Automated Detection of Activity Onset after Postictal Generalized EEG Suppression, BMC Med. Inform. Decis. Mak., vol. 20, no. 12, art. 327, Dec. 2020.
  27. A. G. Marques, S. Segarra, and G. Mateos, Signal Processing on Directed Graphs, IEEE Signal Process. Magazine, vol. 37, no. 6, pp. 99-116, Nov. 2020. Arxiv Preprint
  28. T. M. Roddenberry, M. T. Schaub, H. T. Wai, and S. Segarra, Exact Blind Community Detection from Signals on Multiple Graphs, IEEE Trans. Signal Process., vol. 68, pp. 5016-5030, 2020. Arxiv Preprint
  29. Y. Wang, S. Segarra, and C. Uhler, High-Dimensional Joint Estimation of Multiple Directed Gaussian Graphical Models, Electronic J. Stat., vol. 14, no. 1, pp. 2439-2483, 2020. Arxiv Preprint
  30. Y. Zhu, F. J. Iglesias, A. G. Marques, and S. Segarra, Estimating Network Processes via Blind Identification of Multiple Graph Filters, IEEE Trans. Signal Process., vol. 68, pp. 3049-3063, 2020. Arxiv Preprint
  31. Y. Zhu, M. T. Schaub, A. Jadbabaie, and S. Segarra, Network Inference from Consensus Dynamics with Unknown Parameters, IEEE Trans. Signal and Info. Process. over Networks, vol. 6, pp. 300-315, 2020. Arxiv Preprint
  32. M. T. Schaub, S. Segarra, and J. N. Tsitsiklis, Blind Identification of Stochastic Block Models from Dynamical Observations, SIAM J. Math. Data Science (SIMODS), vol. 2, no. 2, pp. 335–367, 2020. Arxiv Preprint
  33. H. T. Wai, S. Segarra, A. E. Ozdaglar, A. Scaglione, and A. Jadbabaie, Blind Community Detection from Low-rank Excitations of a Graph Filter, IEEE Trans. Signal Process., vol. 68, pp. 436 – 451, 2020. Arxiv Preprint
  34. *M. Avella-Medina, F. Parise, M. T. Schaub, and S. Segarra, Centrality Measures for Graphons: Accounting for Uncertainty in Networks, IEEE Trans. Network Science and Eng., vol. 7, no. 1, pp. 520-537, Jan. 2020. Arxiv Preprint
  35. S. Segarra, M. Eisen, G. Egan, and A. Ribeiro, A Response to Pervez Rizvi’s Critique of the Word Adjacency Method for Authorship Attribution, ANQ: A Quarterly Journal of Short Articles, Notes and Reviews, vol. 33, no.4, pp. 332-337, 2019.
  36. G. Mateos, S. Segarra, A. G. Marques, and A. Ribeiro, Connecting the Dots: Identifying Network Structure via Graph Signal Processing, IEEE Signal Process. Magazine, vol. 36, no. 3, pp. 16-43, May 2019. Arxiv Preprint
  37. M. Eisen, A. Ribeiro, S. Segarra, and G. Egan, Stylometric analysis of Early Modern period English plays, Digital Scholarship in the Humanities, vol. 33, no. 3, pp. 500-528, Sept. 2018. Arxiv Preprint
  38. F. Gama, S. Segarra, and A. Ribeiro, Hierarchical Overlapping Clustering of Network Data Using Cut Metrics, IEEE Trans. Signal and Info. Process. over Networks, vol. 4, no. 2, pp. 392-406, June 2018. Arxiv Preprint
  39. *G. Carlsson, F. Memoli, A. Ribeiro, and S. Segarra, Hierarchical Clustering of Asymmetric Networks, Advances in Data Analysis and Classification, vol. 12, no. 1, pp. 65-105, Mar. 2018. Arxiv Preprint
  40. *G. Carlsson, F. Memoli, A. Ribeiro, and S. Segarra, Admissible Hierarchical Clustering Methods and Algorithms for Asymmetric Networks, IEEE Trans. Signal and Info. Process. over Networks, vol. 3, no. 4, pp. 711-727, Dec. 2017. Arxiv Preprint
  41. A. G. Marques, S. Segarra, G. Leus, and A. Ribeiro, Stationary Graph Processes and Spectral Estimation, IEEE Trans. Signal Process., vol. 65, no. 22, pp. 5911-5926, Nov. 2017. Arxiv Preprint
  42. J. D. Medaglia, W. Huang, S. Segarra, C. Olm, J. Gee, M. Grossman, A. Ribeiro, C. T. McMillan, and D. S. Bassett, Brain Network Efficiency is Influenced by Pathological Source of Corticobasal Syndrome, Neurology, vol. 89, no. 13, pp 1373-1381, Sept. 2017.
  43. S. Segarra, A. G. Marques, G. Mateos, and A. Ribeiro, Network Topology Inference from Spectral Templates, IEEE Trans. Signal and Info. Process. over Networks, vol. 3, no. 3, pp. 467-483, Sept. 2017. Arxiv Preprint IEEE SPS Young Author Best Paper Award
  44. S. Segarra, A. G. Marques, and A. Ribeiro, Optimal Graph-Filter Design and Applications to Distributed Linear Network Operators, IEEE Trans. Signal Process., vol. 65, no. 15, pp. 4117-4131, Aug. 2017. [corresponds to Arxiv preprint Distributed Linear Network Operators using Graph Filters]
  45. S. Segarra, G. Mateos, A. G. Marques, and A. Ribeiro, Blind Identification of Graph Filters, IEEE Trans. Signal Process., vol. 65, no. 5, pp. 1146-1159, March 2017. Arxiv Preprint
  46. S. Segarra, M. Eisen, G. Egan, and A. Ribeiro, Attributing the Authorship of the Henry VI Plays by Word Adjacency, Shakespeare Quarterly, vol. 67 no. 2, pp. 232-256, 2016.
  47. S. Segarra, A. G. Marques, G. Leus, and A. Ribeiro, Reconstruction of Graph Signals through Percolation from Seeding Nodes, IEEE Trans. Signal Process., vol. 64, no. 16, pp. 4363 – 4378, Aug. 2016. Arxiv Preprint
  48. A. G. Marques, S. Segarra, G. Leus, and A. Ribeiro, Sampling of Graph Signals with Successive Local Aggregations, IEEE Trans. Signal Process., vol. 64, no. 7, pp. 1832 – 1843, Apr. 2016. Arxiv Preprint
  49. S. Segarra and A. Ribeiro, Stability and Continuity of Centrality Measures in Weighted Graphs, IEEE Trans. Signal Process., vol. 64, no. 3, pp. 543-555, Feb. 2016. Arxiv Preprint
  50. S. Segarra, M. Eisen, and A. Ribeiro, Authorship Attribution through Function Word Adjacency Networks, IEEE Trans. Signal Process., vol. 63, no. 20, pp. 5464-5478, Oct. 2015. Arxiv Preprint
  51. S. Segarra, W. Huang, and A. Ribeiro, Diffusion and Superposition Distances for Signals Supported on Networks, IEEE Trans. Signal and Info. Process. over Networks, vol. 1, no. 1, pp. 20-32, March 2015. Arxiv Preprint

Conference Papers

  1. C. Li, W. Chen, and S. Segarra, Topology Inference in Evolutionary Games on Graphs, preprint, 2023.
  2. Z. Zhao, A. Swami, and S. Segarra, Graph-based Deterministic Policy Gradient for Repetitive Combinatorial Optimization Problems, Intl. Conf. Learning Representations (ICLR), 2023.
  3. N. Zilberstein, C. Dick, R. Doost-Mohammady, A. Sabharwal, and S. Segarra, Accelerated Massive MIMO Detector based on Annealed Underdamped Langevin Dynamics, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), 2023.
  4. Z. Zhao, B. Radojicic, G. Verma, A. Swami, and S. Segarra, Delay-Aware Backpressure Routing Using Graph Neural Networks, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), 2023.
  5. N. Glaze, A. Bayer, X. Jiang, S. Savitz, and S. Segarra, Graph Representation Learning For Stroke Recurrence Prediction, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), 2023.
  6. M. Navarro and S. Segarra, GraphMAD: Graph Mixup for Data Augmentation Using Data-Driven Convex Clustering, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), 2023.
  7. T. M. Roddenberry, V. P. Grande, F. Frantzen, M. T. Schaub, and S. Segarra, Signal Processing On Product Spaces, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), 2023.
  8. T. M. Roddenberry and S. Segarra, Windowed Fourier Analysis for Signal Processing on Graph Bundles, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), 2023.
  9. L. O’Bryan, S. Segarra, J. Paoletti, S. Zajac, M. E. Beier, A. Sabharwal, M. Wettergreen, and E. Salas, Conversational Turn-taking as a Stochastic Process on Networks, Asilomar Conference on Signals, Systems, and Computers, pp. 1243-1247, 2022
  10. Y. Zhu, B. Li, and S. Segarra, Hypergraph 1-Spectral Clustering with General Submodular Weights, Asilomar Conference on Signals, Systems, and Computers, pp. 935-939, 2022.
  11. S. Rey, M. Navarro, A. Buciulea, S. Segarra, and A. G. Marques, Joint Graph Learning from Gaussian Observations in the Presence of Hidden Nodes, Asilomar Conference on Signals, Systems, and Computers, pp. 53-57, 2022.
  12. P. Namyar, B. Arzani, R. Beckett, S. Segarra, H. Raj, and S. Kandula, Minding the Gap between Fast Heuristics and their Optimal Counterparts, ACM Workshop on Hot Topics in Networks, pp. 138-144, 2022.
  13. B. Coleman, S. Segarra, A. Shrivastava, and A Smola, Graph Reordering for Cache-Efficient Near Neighbor Search, Conf. on Neural Info. Process. Systems (NeurIPS), pp. 38488–38500, 2022.
  14. N. Zilberstein, C. Dick, R. Doost-Mohammady, A. Sabharwal, and S. Segarra, Detection by Sampling: Massive MIMO Detector based on Langevin Dynamics, European Signal Process. Conf. (EUSIPCO), pp. 1651-1655, 2022.
  15. N. Zilberstein, C. Dick, R. Doost-Mohammady, A. Sabharwal, and S. Segarra, Robust MIMO Detection using Hypernetworks with Learned Regularizers, European Signal Process. Conf. (EUSIPCO), pp. 1626- 1630, 2022.
  16. Z. Zhao, A. Swami, and S. Segarra, Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 5308-5312, 2022.
  17. B. Li, A. Swami, and S. Segarra, Power Allocation for Wireless Federated Learning using Graph Neural Networks, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 5243-5247, 2022. 
  18. T. M. Roddenberry, F. Frantzen, M. T. Schaub, and S. Segarra, Hodgelets: Localized Spectral Representations of Flows on Simplicial Complexes, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 5922-5926, 2022. 
  19. M. Navarro and S. Segarra, Graphon-aided Joint Estimation of Multiple Graphs, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 5458-5462, 2022. 
  20. Y. Zhu and S. Segarra, Hypergraph Cuts with Edge-Dependent Vertex Weights: Spectral Clustering Based on the 1-Laplacian, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 8837-8841, 2022.
  21. Z. Zhao, G. Verma, A. Swami, and S. Segarra, Delay-Oriented Distributed Scheduling using Graph Neural Networks, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 8902-8906, 2022.
  22. A. Chowdhury, F. Gama, and S. Segarra, Stability Analysis of Unfolded WMMSE for Power Allocation, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 5298-5302, 2022.
  23. F. Gama, N. Zilberstein, R. G. Baraniuk, and S. Segarra, Unrolling Particles: Unsupervised Learning of Sampling Distributions, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 5498-5502, 2022.
  24. S. Rey, A. Buciulea, M. Navarro, S. Segarra, and A. G. Marques, Joint Inference of Multiple Graphs with Hidden Variables from Stationary Graph Signals, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 5817-5821, 2022.
  25. A. Bayer, A. Chowdhury, and S. Segarra, Label Propagation across Graphs: Node Classification using Graph Neural Tangent Kernels, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 5483-5487, 2022.
  26. A. Chowdhury, G. Verma, C. Rao, A. Swami, and S. Segarra, ML-aided Power Allocation for Tactical MIMO, Military Comm. Conf. (MILCOM), pp. 273-278, 2021.
  27. Y. Zhu, A. Swami, and S. Segarra, Node Embedding based on the Free Energy Distance, Asilomar Conference on Signals, Systems, and Computers, pp. 187-191, 2021.
  28. B. Li, G. Verma, C. Rao, and S. Segarra, Energy-Efficient Power Allocation in Wireless Networks using Graph Neural Networks, Asilomar Conference on Signals, Systems, and Computers, pp. 732-736, 2021.
  29. V. M. Tenorio, S. Rey, F. Gama, S. Segarra, and A. G. Marques, A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood Filters, Asilomar Conference on Signals, Systems, and Computers, pp. 1573-1578, 2021.
  30. G. Cutura, B. Li, A. Swami, and S. Segarra, Deep Demixing: Reconstructing the Evolution of Epidemics Using Graph Neural Networks, European Signal Process. Conf. (EUSIPCO), pp. 2204-2208, 2021.
  31. Y. Zhu, B. Li, and S. Segarra, Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning, European Signal Process. Conf. (EUSIPCO), pp. 1416-1420, 2021. 
  32. T. M. Roddenberry, N. Glaze, and S. Segarra, Principled Simplicial Neural Networks for Trajectory Prediction, International Conference on Machine Learning (ICML), vol. 139, pp. 9020-9029, 2021.
  33. T.M. Roddenberry, S. Segarra, and A Kyrillidis, Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets, 2021.
  34. A. Chowdhury, G. Verma, C. Rao, A. Swami, and S. Segarra, Efficient Power Allocation using Graph Neural Networks and Deep Algorithm Unfolding, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 4725-4729, Toronto, Canada, June 6-11, 2021.
  35. A. Kumar, G. Verma, C. Rao, A. Swami, and S. Segarra, Adaptive Contention Window Design using Deep Q-learning, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 4950-4954, Toronto, Canada, June 6-11, 2021.
  36. Z. Zhao, G. Verma, C. Rao, A. Swami, and S. Segarra, Distributed Scheduling using Graph Neural Networks, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 4720-4724, Toronto, Canada, June 6-11, 2021.
  37. T. M. Roddenberry, M. Navarro, and S. Segarra, Network Topology Inference with Graphon Spectral Penalties, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 5390-5394, Toronto, Canada, June 6-11, 2021.
  38. C. Kaushik, T. M. Roddenberry, and S. Segarra, Network Topology Change-Point Detection from Graph Signals with Prior Spectral Signatures, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 5395-5399, Toronto, Canada, June 6-11, 2021.
  39. T. M. Roddenberry and S. Segarra, Blind Estimation of Eigenvector Centrality from Graph Signals: Beyond Low-pass Filtering, Asilomar Conference on Signals, Systems, and Computers, pp. 465-469, Pacific Grove, CA, November 1-4, 2020. Best Student Paper Award.
  40. T. M. Roddenberry and S. Segarra, Blind Inference of Centrality Rankings from Graph Signals, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 5335-5339, Barcelona, Spain, May 4-8, 2020.
  41. A. Madapu, S. Segarra, S. P. Chepuri, and A. G. Marques, Generative Adversarial Networks For Graph Data Imputation From Signed Observations, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 9085-9089, Barcelona, Spain, May 4-8, 2020.
  42. S. Segarra, T. M. Roddenberry, F. Memoli, and A. Ribeiro, Metric Representations of Networks: A Uniqueness Result, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 5345-5349, Barcelona, Spain, May 4-8, 2020.
  43. S. Rey, A. G. Marques, and S. Segarra, An Underparametrized Deep Decoder Architecture for Graph Signals, IEEE Intl. Wrksp. on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 231-235, Guadaloupe, West Indies, December 15-18, 2019.
  44. T. M. Roddenberry and S. Segarra, HodgeNet: Graph Neural Networks for Edge Data, Asilomar Conference on Signals, Systems, and Computers, pp. 220-224, Pacific Grove, CA, November 3-6, 2019.
  45. J. Jia, M. T. Schaub, S. Segarra, and A. R. Benson, Graph-based Semi-supervised & Active Learning for Edge Flows, ACM SIGKDD Conf. Knowledge Disc. and Data Min. (KDD), Anchorage, AK, August 4-8, 2019.
  46. C. Heghedus, S. Segarra, A. Chakravorty, and C. Rong, Neural Network Architectures for Electricity Consumption Forecasting, IEEE Intl. Conf. Green Computin Comm. (GreenCom), pp. 776-783, Atlanta, GA, July 14-17, 2019.
  47. *M. T. Schaub, S. Segarra, and H.T. Wai, Spectral Partitioning of Time-Varying Networks with Unobserved Edges, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 4938-4942, Brighton, UK, May 12-17, 2019. Arxiv Preprint
  48. Y. Zhu, F. J. Iglesias, A. G. Marques, and S. Segarra, Estimation of Network Processes via Blind Graph Multi-Filter Identification, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 5451-5455, Brighton, UK, May 12-17, 2019.
  49. M. T. Schaub and S. Segarra, Flow Smoothing and Denoising: Graph Signal Processing in the Edge-Space, IEEE Global Conf. Signal and Info. Process. (GlobalSIP), pp. 735-739, Anaheim, CA, November 29-29, 2018. Arxiv Preprint
  50. J. Yang and S. Segarra, Enhancing Geometric Deep Learning via Graph Filter Deconvolution, IEEE Global Conf. Signal and Info. Process. (GlobalSIP), pp. 758-762, Anaheim, CA, November 26-29, 2018. Arxiv Preprint
  51. S. Segarra, A. G. Marques, M. Goyal, and S. Rey, Network Topology Inference from Input-Output Diffusion Pairs, IEEE Statistical Signal Processing Workshop, pp. 508-512, Freiburg im Breisgau, Germany, June 10-13, 2018.
  52. R. Shafipour, S. Segarra, A. G. Marques, and G. Mateos, Directed Network Topology Inference via Graph Filter Identification, IEEE Data Science Workshop, pp. 210-214, Lausanne, Switzerland, June 4-6, 2018.
  53. H. T. Wai, S. Segarra, A. E. Ozdaglar, A. Scaglione, and A. Jadbabaie, Community Detection from Low-rank Excitations of Graph Filters, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 4044-4048, Calgary, Canada, April 15-20, 2018. Best Student Paper Award.
  54. R. Shafipour, S. Segarra, A. G. Marques, and G. Mateos, Identifying Undirected Network Structure via Semidefinite Relaxation, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 4049-4053, Calgary, Canada, April 15-20, 2018.
  55. F. J. Iglesias, S. Segarra, S. Rey, A. G. Marques, and D. Ramirez, Demixing and Blind Deconvolution of Graph-diffused Sparse Signals, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 4189-4193, Calgary, Canada, April 15-20, 2018.
  56. S. Segarra, M. T. Schaub, and A. Jadbabaie, Network Inference from Consensus Dynamics, IEEE Conf. on Decision and Control, pp. 3212-3217, Melbourne, Australia, December 12-15, 2017. Arxiv Preprint
  57. S. Segarra, A. G. Marques, G. Arce, and A. Ribeiro, Design of Weighted Median Graph Filters, IEEE Intl. Wrksp. on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 1-5, Curaçao, Dutch Antilles, December 10-13, 2017.
  58. S. Segarra, Y. Wang, C. Uhler, and A. G. Marques, Joint Inference of Networks from Stationary Graph Signals, Asilomar Conference on Signals, Systems, and Computers, pp. 975-979, Pacific Grove, CA, October 29 – November 1, 2017.
  59. S. Segarra, A. G. Marques, G. Leus, and A. Ribeiro, Stationary Graph Processes: Parametric Power Spectral Estimation, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 4099-4103, New Orleans, USA, March 5-9, 2017.
  60. D. Ramirez, A. G. Marques, and S. Segarra, Graph-Signal Reconstruction and Blind Deconvolution for Diffused Sparse Inputs, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 4104-4108, New Orleans, USA, March 5-9, 2017.
  61. R. Shafipour, S. Segarra, A. G. Marques, and G. Mateos, Network Topology Inference from Non-Stationary Graph Signals, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 5870-5874, New Orleans, USA, March 5-9, 2017.
  62. S. Segarra, A. G. Marques, G. Mateos, and A. Ribeiro, Robust Network Topology Inference, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 6518-6522, New Orleans, USA, March 5-9, 2017.
  63. S. Segarra, A. G. Marques, G. Arce, and A. Ribeiro, Center-Weighted Median Graph Filters, IEEE Global Conf. Signal and Info. Process. (GlobalSIP), pp. 336-340, Washington DC, December 7-9, 2016.
  64. S. Segarra, A. G. Marques, G. Mateos, and A. Ribeiro, Network Topology Identification from Imperfect Spectral Templates, Asilomar Conference on Signals, Systems, and Computers, pp. 1465-1469, Pacific Grove, CA, November 6-9, 2016.
  65. S. Segarra, A. G. Marques, G. Leus, and A. Ribeiro, Stationary Graph Processes: Nonparametric Power Spectral Estimation, IEEE Sensor Array and Multichannel Signal Process. Wrksp., pp. 1-5, Rio de Janeiro, Brazil, July 10-13, 2016. Best Paper Award.
  66. S. Segarra, A. G. Marques, G. Mateos, and A. Ribeiro, Network Topology Identification from Spectral Templates, IEEE Intl. Wrksp. on Statistical Signal Process., pp. 1-5, Palma de Mallorca, Spain, June 26-29, 2016. Best Student Paper Award.
  67. S. Segarra, A. G. Marques, G. Mateos, and A. Ribeiro, Blind Identification of Graph Filters with Multiple Sparse Inputs, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 4099-4103, Shanghai China, March 20-25, 2016.
  68. S. Segarra, A. G. Marques, G. Leus, and A. Ribeiro, Space-Shift Sampling of Graph Signals, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 6355-6359, Shanghai China, March 20-25, 2016.
  69. S. Segarra, A. G. Marques, and A. Ribeiro, Linear Network Operators Using Node-Variant Graph Filters, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 4850-4854, Shanghai China, March 20-25, 2016.
  70. F. Gama, S. Segarra, and A. Ribeiro, Overlapping Clustering of Network Data Using Cut Metrics, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 6415-6419, Shanghai China, March 20-25, 2016.
  71. J. Ma, W. Huang, S. Segarra, and A. Ribeiro, Diffusion Filtering for Graph Signals and Its Use in Recommendation Systems, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 4563-4567, Shanghai China, March 20-25, 2016.
  72. S. Segarra, G. Mateos, A. G. Marques, and A. Ribeiro, Blind Identification of Graph Filters with Sparse Inputs, IEEE Intl. Wrksp. on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 449-452, Cancun, Mexico, Dec. 13-16, 2015.
  73. S. Segarra, A. G. Marques, G. Leus, and A. Ribeiro, Aggregation Sampling of Graph Signals in the Presence of Noise, IEEE Intl. Wrksp. on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 101-104, Cancun, Mexico, Dec. 13-16, 2015. Version with appendix.
  74. S. Segarra, A. G. Marques, G. Leus, and A. Ribeiro, Reconstruction of Graph Signals: Percolation from a Single Seeding Node, IEEE Global Conf. Signal and Info. Process. (GlobalSIP), pp. 844-848, Orlando, FL, December 14-16, 2015. Version with appendix.
  75. S. Segarra, A. G. Marques, G. Leus, and A. Ribeiro, Sampling of Graph Signals: Successive Local Aggregations at a Single Node, Asilomar Conference on Signals, Systems, and Computers, pp. 1819-1823, Pacific Grove, CA, November 8-11, 2015. Best Student Paper Award.
  76. W. Huang, S. Segarra, and A. Ribeiro, Diffusion Distance for Signals Supported on Networks, Asilomar Conference on Signals, Systems, and Computers, pp. 1219-1223, Pacific Grove, CA, November 8-11, 2015.
  77. S. Segarra, A. G. Marques, and A. Ribeiro, Distributed Implementation of Network Linear Operators using Graph Filters, Allerton Conf. on Commun. Control and Computing, pp. 1406-1413, Univ. of Illinois at U-C, Monticello, IL, Sept. 30-Oct. 2, 2015.
  78. S. Segarra, A. G. Marques, G. Leus, and A. Ribeiro, Interpolation of graph signals using shift-invariant graph filters, European Signal Process. Conf. (EUSIPCO), pp. 210-214, Nice, France, August 31 – September 4, 2015. Version with appendix.
  79. S. Segarra and A. Ribeiro, Stability and Continuity of Centrality Measures in Weighted Graphs, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 3387-3391, Brisbane, Australia, April 19-24, 2015.
  80. S. Segarra and A. Ribeiro, Dithering and Betweenness Centrality in Weighted Graphs, IEEE Global Conf. Signal and Info. Process. (GlobalSIP), pp. 847-851, Atlanta, GA, December 3-5, 2014.
  81. *G. Carlsson, F. Memoli, A. Ribeiro, and S. Segarra, Hierarchical Quasi-Clustering Methods for Asymmetric Networks, JMLR W&CP: International Conference on Machine Learning (ICML), vol. 32, pp. 352-360, Beijing, China, June 21-26, 2014.
  82. S. Segarra and A. Ribeiro, A Stable Betweenness Centrality Measure in Networks, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp. 3859-3863, Florence, Italy, May 4-9, 2014.
  83. S. Segarra and A. Ribeiro, Hierarchical Clustering and Consensus in Trust Networks, IEEE Intl. Wrksp. on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp.85-88, Saint Martin, Dec. 15-18, 2013. Version with appendix.
  84. *G. Carlsson, F. Memoli, A. Ribeiro, and S. Segarra, Alternative Axiomatic Constructions for Hierarchical Clustering of Asymmetric Networks, IEEE Global Conf. Signal and Info. Process. (GlobalSIP), pp.791-794, Austin, TX, Dec. 3-5, 2013.
  85. *G. Carlsson, F. Memoli, A. Ribeiro, and S. Segarra, Hierarchical Clustering Methods and Algorithms for Asymmetric Networks, Asilomar Conference on Signals, Systems, and Computers, pp. 1773-1777, Pacific Grove, CA, November 3-6, 2013.
  86. *G. Carlsson, F. Memoli, A. Ribeiro, and S. Segarra, Axiomatic Construction of Hierarchical Clustering in Asymmetric Networks, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp.5219-5223, Vancouver, Canada, May 26-31, 2013.
  87. S. Segarra, M. Eisen, and A. Ribeiro, Authorship Attribution using Function Words Adjacency Networks, IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP), pp.5563-5567, Vancouver, Canada, May 26-31, 2013.

Book Chapters

  1. M. T. Schaub, J.-B. Seby, F. Frantzen, T. M. Roddenberry, Y. Zhu, and S. Segarra, Signal Processing on Simplicial Complexes, in Higher-Order Systems,  F. Battiston and G. Petri (Eds.), Springer, 2022
  2. S. Segarra, S. P. Chepuri, A. G. Marques, and G. Leus, Statistical Graph Signal Processing: Stationarity and Spectral Estimation, in Cooperative and Graph Signal Processing, P. M. Djuric and C. Richard (Eds.), Elsevier, 2018.
  3. G. Mateos, S. Segarra, and A. G. Marques, Inference of Graph Topology, in Cooperative and Graph Signal Processing, P. M. Djuric and C. Richard (Eds.), Elsevier, 2018.
  4. *G. Carlsson, F. Memoli, A. Ribeiro, and S. Segarra, Representable Hierarchical Clustering Methods for Asymmetric Networks, in Data Science: Innovative Developments in Data Analysis and Clustering, F. Palumbo, A. Montanari, and M. Vichi (Eds.), Springer, 2017. [book is a compilation of selected papers presented at the 2015 Conference of the International Federation of Classification Societies].

Theses

  1. S. Segarra, Metric Representations of Networks, Doctoral Thesis, 2016. Joseph and Rosaline Wolf Award for Best Doctoral Dissertation.
  2. S. Segarra, Word Adjacency Networks for Authorship Attribution: Solving Shakespearean Controversies, Master Thesis, 2014.