Publications

See also my Google Scholar profile. A full list is in my CV.

Journal articles

  1. A. Karaaslanli, P. A. Traganitis, and A. Konar, “Dense Subgraph Detection for Crowdsourcing with Colluding Adversaries,” IEEE Transactions on Signal Processing (submitted).
  2. M. Eini, A. Karaaslanli, V. Kalantzis, and P. A. Traganitis, “Subspace Projection Methods for Fast Spectral Embeddings of Evolving Graphs,” IEEE Transactions on Signal and Information Processing over Networks (submitted). arXiv
  3. S. Ibrahim, P. A. Traganitis, X. Fu, and G. B. Giannakis, “Learning from Crowdsourced Noisy Labels: A Signal Processing Perspective,” IEEE Signal Processing Magazine, vol. 42, no. 3, pp. 84–106, May 2025. arXiv
  4. P. A. Traganitis and G. B. Giannakis, “Unsupervised Ensemble Classification with Sequential and Networked Data,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 10, pp. 5009–5022, Oct. 2022. arXiv
  5. Y. Shen, P. A. Traganitis, and G. B. Giannakis, “Graph-Adaptive Nonlinear Dimensionality Reduction,” IEEE Transactions on Signal Processing (submitted).
  6. P. A. Traganitis, A. Pagès-Zamora, and G. B. Giannakis, “Blind Multiclass Ensemble Classification,” IEEE Transactions on Signal Processing, vol. 66, pp. 4737–4752, Sept. 2018. arXiv
  7. P. A. Traganitis and G. B. Giannakis, “Sketched Subspace Clustering,” IEEE Transactions on Signal Processing, vol. 66, no. 7, pp. 1663–1675, Apr. 2018. arXiv · MATLAB code
  8. P. A. Traganitis, K. Slavakis, and G. B. Giannakis, “Sketch and Validate for Big Data Clustering,” IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 4, pp. 678–690, June 2015. arXiv

Conferences and workshops

  1. M. Eini, A. Karaaslanli, V. Kalantzis, and P. A. Traganitis, “Efficient Estimation of Time-varying Signals over Evolving Graphs,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Oct. 2026.
  2. A. Chudda and P. A. Traganitis, “Aggregating Multiple Self-Supervised Tasks for Robust Semi-Supervised Learning,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Oct. 2026.
  3. A. Karaaslanli, M. Eini, P. A. Traganitis, and V. Kalantzis, “Efficient Spectral Embeddings of Graph Edges,” Proc. of European Signal Processing Conf., Bruges, Belgium, Sept. 2026.
  4. Y. Zhang, Q. Lu, P. A. Traganitis, and G. B. Giannakis, “Meta-learning Gaussian Process Priors Using Non-stationary Spectral Features,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Oct. 2025.
  5. A. Karaaslanli, S. Aviyente, and P. A. Traganitis, “Simultaneous Detection of Anomalous Nodes and Edges in Graphs,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Oct. 2025.
  6. H. Wang and P. A. Traganitis, “Learning from Multiple Noisily Optimal Demonstrators in Stochastic Multi-Armed Bandits,” Proc. of IEEE Intl. Workshop on Machine Learning for Signal Processing, Istanbul, Turkey, Sept. 2025.
  7. A. Karaaslanli, P. A. Traganitis, and A. Konar, “Identifying Adversarial Attacks in Crowdsourcing via Dense Subgraph Detection,” Proc. of ICASSP, Hyderabad, India, Apr. 2025.
  8. V. Kalantzis, P. A. Traganitis, and C. I. Kanatsoulis, “Online Canonical Correlation Analysis via Rayleigh-Ritz Projections,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Oct. 2024.
  9. A. Karaaslanli, P. A. Traganitis, and S. Aviyente, “Identifying Anomalous Edges with Link Sampling and Consensus,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Oct. 2024.
  10. K. D. Polyzos, P. A. Traganitis, M. K. Singh, and G. B. Giannakis, “Ensembles of Informative Representations for Self-Supervised Learning,” Proc. of IEEE Machine Learning for Signal Processing Workshop, London, UK, Sept. 2024. (Best paper award, second place)
  11. G. Kollias, V. Kalantzis, L. Horesh, S. Ubaru, and P. A. Traganitis, “Counting Triangles of Graphs via Matrix Partitioning,” Proc. of IEEE Machine Learning for Signal Processing Workshop, London, UK, Sept. 2024.
  12. P. A. Traganitis and C. I. Kanatsoulis, “A Graph Autoencoder Approach to Crowdsourcing,” Proc. of IEEE Sensor Array and Multichannel Signal Processing Workshop, Corvallis, OR, July 2024.
  13. V. Kalantzis and P. A. Traganitis, “Rayleigh-Ritz Based Updates of the Multilinear Singular Value Decomposition,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Oct. 2023.
  14. P. A. Traganitis and E. G. Strangas, “Perspectives of Transfer Learning on the Diagnosis of Faults in Electrical Machines, Power Electronics, and Drives,” Proc. of Intl. Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, Chania, Greece, Aug. 2023.
  15. M. K. Singh, K. D. Polyzos, P. A. Traganitis, S. V. Dhople, and G. B. Giannakis, “Physics-Informed Transfer Learning for Voltage Stability Margin Prediction,” Proc. of ICASSP, Rhodes, Greece, June 2023.
  16. V. Kalantzis and P. A. Traganitis, “Matrix Resolvent Eigenembeddings for Dynamic Graphs,” Proc. of ICASSP, Rhodes, Greece, June 2023.
  17. P. A. Traganitis and G. B. Giannakis, “Identifying Dependent Annotators in Crowdsourcing,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Oct. 2022.
  18. P. A. Traganitis and G. B. Giannakis, “Detecting Adversaries in Crowdsourcing,” Proc. of IEEE ICDM, Auckland, New Zealand, Dec. 2021. arXiv
  19. P. A. Traganitis and G. B. Giannakis, “Bayesian Constrained Decision Fusion,” Proc. of IEEE SPAWC, Lucca, Italy, Sept. 2021.
  20. P. A. Traganitis and G. B. Giannakis, “Bayesian Crowdsourcing with Constraints,” Proc. of ECML-PKDD, Bilbao, Spain, Sept. 2021.
  21. P. A. Traganitis and G. B. Giannakis, “Identifying Spammers to Boost Crowdsourced Classification,” Proc. of ICASSP, Toronto, Canada, June 2021.
  22. P. A. Traganitis and G. B. Giannakis, “Constrained Clustering using Gaussian Processes,” Proc. of European Signal Processing Conf., Amsterdam, Netherlands, Jan. 2021.
  23. P. A. Traganitis, D. Berberidis, and G. B. Giannakis, “Active Learning with Unsupervised Ensembles of Classifiers,” Proc. of ICASSP, Barcelona, Spain, May 2020.
  24. P. A. Traganitis, “Blind Ensemble Classification of Sequential Data,” Proc. of IEEE Data Science Workshop, Minneapolis, MN, June 2019.
  25. P. A. Traganitis and G. B. Giannakis, “Blind Multi-class Ensemble Learning with Dependent Classifiers,” Proc. of EUSIPCO, Rome, Italy, Sept. 2018.
  26. V. N. Ioannidis, P. A. Traganitis, Y. Shen, and G. B. Giannakis, “Kernel-Based Semi-Supervised Learning Over Multilayer Graphs,” Proc. of IEEE SPAWC, Kalamata, Greece, June 2018.
  27. P. A. Traganitis and G. B. Giannakis, “Distributed Sketched Subspace Clustering for Large-Scale Data,” Proc. of CAMSAP, Curaçao, Dec. 2017. (Invited)
  28. Y. Shen, P. A. Traganitis, and G. B. Giannakis, “Nonlinear Dimensionality Reduction on Graphs,” Proc. of CAMSAP, Curaçao, Dec. 2017. (Invited) (Student paper award finalist) arXiv
  29. P. A. Traganitis, A. Pagès-Zamora, and G. B. Giannakis, “Learning from Unequally Reliable Blind Ensembles of Classifiers,” Proc. of GlobalSIP, Montreal, Canada, Nov. 2017.
  30. P. A. Traganitis, Y. Shen, and G. B. Giannakis, “Network Topology Inference via Elastic Net Structural Equation Models,” Proc. of EUSIPCO, Kos, Greece, Aug. 2017.
  31. P. A. Traganitis, Y. Shen, and G. B. Giannakis, “Topology Inference for Multilayer Networks,” Proc. of Intl. Workshop on Network Science for Communications, May 2017. (ARO/ARL travel grant)
  32. P. A. Traganitis and G. B. Giannakis, “PARAFAC-based Multilinear Subspace Clustering for Tensor Data,” Proc. of GlobalSIP, Washington, DC, Dec. 2016. (NSF travel grant)
  33. P. A. Traganitis and G. B. Giannakis, “A Randomized Approach to Large-Scale Subspace Clustering,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 2016. (Invited)
  34. P. A. Traganitis and G. B. Giannakis, “Efficient Subspace Clustering of Large-scale Data Streams with Misses,” Proc. of Conf. on Information Sciences and Systems, Princeton, NJ, Mar. 2016.
  35. P. A. Traganitis, K. Slavakis, and G. B. Giannakis, “Large-scale Subspace Clustering using Random Sketching and Validation,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 2015. arXiv
  36. P. A. Traganitis, K. Slavakis, and G. B. Giannakis, “Spectral Clustering of Large-scale Communities via Random Sketching and Validation,” Proc. of Conf. on Information Sciences and Systems, Baltimore, MD, Mar. 2015.
  37. P. A. Traganitis, K. Slavakis, and G. B. Giannakis, “Clustering High-Dimensional Data via Random Sampling and Consensus,” Proc. of GlobalSIP, Atlanta, GA, Dec. 2014.
  38. P. A. Traganitis, K. Slavakis, and G. B. Giannakis, “Big Data Clustering using Random Sampling and Consensus,” Proc. of Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 2014.

Book chapters

  1. B. Baingana, P. A. Traganitis, G. Mateos, and G. B. Giannakis, “Big Data Analytics for Social Networks,” in Graph Analysis for Social Media, I. Pitas, Ed., CRC Press, 2015.

Tutorials

  • “Learning with Noisy Crowdsourced Labels: Insights from Signal Processing,” IEEE Sensor Array and Multichannel Signal Processing Workshop, Shenzhen, China, July 2026 (with X. Fu and G. B. Giannakis).
  • “End-to-End Learning from Crowdsourced Labels: A Signal Processing Perspective,” ICASSP, Hyderabad, India, Apr. 2025 (with S. Ibrahim, X. Fu, and G. B. Giannakis).
  • “Learning from Unreliable Labels via Crowdsourcing,” ICASSP, Rhodes, Greece, June 2023 (with G. B. Giannakis).
  • “Truth Inference in Crowdsensing,” IEEE Intl. Conf. on Communications, Rome, Italy, June 2023 (with G. B. Giannakis).