@article {42575,
	title = {Online Edge Flow Imputation on Networks},
	journal = {IEEE Signal Processing Letters},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway.},
	publisher = {IEEE},
	abstract = {A novel online algorithm for missing data imputation for networks with signals defined on the edges is presented in this paper. Leveraging the prior knowledge intrinsic to most real-world networks, we propose a bi-level optimization scheme that includes (i) a sparse line graph identification strategy by solving a group-Lasso-based optimization framework via composite objective mirror descent to exploit the causal dependencies among the signals and (ii) a Kalman filtering-based signal reconstruction strategy developed using simplicial complex (SC) formulation to exploit the flow conservation. To the best of our knowledge, this is the first SC-based attempt for time-varying signal imputation, whose advantages have been demonstrated through numerical experiments conducted using EPANET models of both synthetic and real water distribution networks.},
	doi = {10.1109/LSP.2022.3221846},
	url = {https://ieeexplore.ieee.org/document/9947283},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar and Isufi, Elvin}
}
@article {42845,
	title = {Scalable and Privacy-aware Online Learning of Nonlinear Structural Equation Models},
	journal = {IEEE Open Journal of Signal Processing},
	year = {2022},
	note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70\&nbsp; from the Research Council of Norway.},
	publisher = {IEEE},
	abstract = {An online topology estimation algorithm for nonlinear structural equation models (SEM) is proposed in this paper, addressing the nonlinearity and the non-stationarity of real-world systems. The nonlinearity is modeled using kernel formulations, and the curse of dimensionality associated with the kernels is mitigated using random feature approximation. The online learning strategy uses a group-lasso-based optimization framework with a prediction-corrections technique that accounts for the model evolution. \E{The proposed approach has three properties of interest. First, it enjoys node-separable learning, which allows for scalability in large networks. Second, it offers privacy in SEM learning by replacing the actual data with node-specific random features. Third, its performance can be characterized theoretically via a dynamic regret analysis, showing that it is possible to obtain a linear dynamic regret bound under mild assumptions. Numerical results with synthetic and real data corroborate our findings and show competitive performance w.r.t. state-of-the-art alternatives.},
	doi = {10.1109/OJSP.2023.3241580},
	url = {https://ieeexplore.ieee.org/document/10034854},
	author = {Money, Rohan and Krishnan, Joshin and Beferull-Lozano, Baltasar and Isufi, Elvin}
}
@article {28413,
	title = {Quantization Analysis and Robust Design in Distributed Graph Filters},
	journal = {IEEE Transactions on Signal Processing},
	volume = {70},
	year = {2021},
	note = {This work is a joint collaboration between SimulaMet\&nbsp; and University of Agder. This work was supported in part by the TOPPFORSK WISECART under Grant 250910/F20 and in part by the IKTPLUSS INDURB under Grant 270730/O70 from the Research Council of Norway.},
	month = {12/2021},
	pages = {643 - 658},
	publisher = { IEEE},
	address = {IEEE Transactions on Signal Processing },
	abstract = {Distributed graph filters have recently found applications in wireless sensor networks (WSNs) to solve distributed tasks such as reaching consensus, signal denoising, and reconstruction. However, when implemented over WSNs, the graph filters should deal with network limited energy constraints as well as processing and communication capabilities.Quantization plays a fundamental role to improve the latter but its effects on distributed graph filtering are little understood. WSNs are also prone to random link losses due to noise and interference. In this instance, the filter output is affected by both the quantization error and the topological randomness error, which, if it is not properly accounted in the filter design phase, may lead to an accumulated error through the filtering iterations and significantly degrade the performance. In this paper, we analyze how quantization affects distributed graph filtering over both time-invariant and time-varying graphs. We bring insights on the quantization effects for the twomost common graph filters: the finite impulse response (FIR) and autoregressive moving average (ARMA) graph filter. Besides providing a comprehensive analysis, we devise theoretical performance guarantees on the filter performancewhen the quantization stepsize is fixed or changes dynamically over the filtering iterations. For FIR filters, we show that a dynamic quantization stepsize leads to more reduction of the quantization noise than in the fixed-stepsize quantization. For ARMAgraph filters,we showthat decreasing the quantization stepsize over the iterations reduces the quantization noise to zero at the steady-state. In addition, we propose robust filter design strategies that minimize the quantization noise for both time-invariant and time-varying networks. Numerical experiments on synthetic and two real data sets corroborate our findings and show the different trade-offs between quantization bits, filter order, and robustness to topological randomness.},
	issn = {1053-587X},
	doi = {10.1109/TSP.2021.3139208},
	url = {https://ieeexplore.ieee.org/document/9665348},
	author = {Ben Saad, Leila and Beferull-Lozano, Baltasar and Isufi, Elvin}
}
