Graph Signal Reconstruction via Koopman Autoencoder
Date
2025
Authors
Krishnan, S.
Park, J.
Choi, J.
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Conference paper
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Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing / sponsored by the Institute of Electrical and Electronics Engineers Signal Processing Society. ICASSP (Conference), 2025, pp.1-5
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Sivaram Krishnan, Jihong Park, and Jinho Choi
Conference Name
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (6 Apr 2025 - 11 Apr 2025 : Hyderabad, India)
Abstract
Real-world graph signals are inherently time-varying and evolve smoothly, making the characterization of such data challenging. We propose a novel approach for reconstructing missing time-varying graph data by leveraging the assumption that the latent variable responsible for generating this data evolves according to nonlinear dynamics. To learn these dynamics, we employ a Koopman autoencoder, and apply graph embedding techniques to map the graph data into a latent space. While existing approaches require known time-invariant Laplacian matrices, our proposed approach can perform reconstruction without needing these matrices, which can also be time-varying. Simulation results show that our method surpasses baseline approaches, achieving a reduction in reconstruction error by 47% to 61% compared to alternative methods, while effectively reconstructing time-varying graphs with dynamic structures.
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