Distributed Autoregressive Moving Average Graph Filters

Loukas, Andreas; Simonetto, Andrea; Leus, Geert

doi:10.1109/lsp.2015.2448655

Cited by 93 publications

(79 citation statements)

References 16 publications

(24 reference statements)

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“…1 scalable consists of substituting the actual filter in Step 1 by a filter that can be distributed over the graph vertices as in [23], [24], [25], [26]. In contrast to these finite impulse response filters, an autoregressive moving average (ARMA) graph filter was proposed in [27], [28]. The parallel ARMA K graph filter is defined by:…”

Section: Distributed and Adaptive Gfssmentioning

confidence: 99%

Distributed Change Detection in Streaming Graph Signals

Ferrari¹,

Richard²,

Verduci³

2019

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Detecting abrupt changes in streaming graph signals is relevant in a variety of applications ranging from energy and water supplies, to environmental monitoring. In this paper, we address this problem when anomalies activate localized groups of nodes in a network. We introduce an online change-point detection algorithm, which is fully distributed across nodes to monitor large-scale dynamic networks. We analyze the detection statistics for controlling the probability of a global type 1 error. Finally we illustrate the detection and localization performance with simulated data.

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Section: Distributed and Adaptive Gfssmentioning

confidence: 99%

Distributed Change Detection in Streaming Graph Signals

Ferrari¹,

Richard²,

Verduci³

2019

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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“…For example, consider distributed average consensus, the task of iteratively averaging all node data through only local network communications [9], which finds use in several applications [10][11][12][13]. Graph filters applied at each node can accelerate consensus convergence [14][15][16][17][18][19][20], which can benefit from asymptotic spectral information for suitable random networks [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Spectral Statistics of Directed Networks With Random Link Model Transpose-Asymmetry

Kruzick

Moura

2018

2018 IEEE Data Science Workshop (DSW)

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Stochastic network influences complicate graph filter design by producing uncertainty in network iteration matrix eigenvalues, the points at which the graph filter response is defined. While joint statistics for the eigenvalues typically elude analysis, predictable spectral asymptotics can emerge for large scale networks. Previously published works successfully analyze large-scale networks described by undirected graphs and directed graphs with transpose-symmetric distributions, focusing on consensus acceleration filter design for timeinvariant networks as an application. This work expands upon these results by enabling analysis of certain large-scale directed networks described by transpose-asymmetric distributions. Specifically, efficiently computable spectral density approximations are possible for transpose-asymmetric percolation network models with node-transitive symmetry group and normal mean matrix. Numerical simulations support the derived approximations and application to consensus filters.

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“…These samples on vertices represent a signal on a graph [6, 7]. From signals on graphs, a new field emerged for the analysis of these data representations called signal processing on graphs [8, 9, 10, 11, 12, 13]. …”

Section: Introductionmentioning

confidence: 99%

A fast algorithm for vertex-frequency representations of signals on graphs

2017

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The windowed Fourier transform (short time Fourier transform) and the S-transform are widely used signal processing tools for extracting frequency information from non-stationary signals. Previously, the windowed Fourier transform had been adopted for signals on graphs and has been shown to be very useful for extracting vertex-frequency information from graphs. However, high computational complexity makes these algorithms impractical. We sought to develop a fast windowed graph Fourier transform and a fast graph S-transform requiring significantly shorter computation time. The proposed schemes have been tested with synthetic test graph signals and real graph signals derived from electroencephalography recordings made during swallowing. The results showed that the proposed schemes provide significantly lower computation time in comparison with the standard windowed graph Fourier transform and the fast graph S-transform. Also, the results showed that noise has no effect on the results of the algorithm for the fast windowed graph Fourier transform or on the graph S-transform. Finally, we showed that graphs can be reconstructed from the vertex-frequency representations obtained with the proposed algorithms.

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Distributed Autoregressive Moving Average Graph Filters

Cited by 93 publications

References 16 publications

Distributed Change Detection in Streaming Graph Signals

Distributed Change Detection in Streaming Graph Signals

Spectral Statistics of Directed Networks With Random Link Model Transpose-Asymmetry

A fast algorithm for vertex-frequency representations of signals on graphs

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