@inproceedings{c23ef5c6d9614c2e81342a1e9e08d7e9,

title = "Stochastic graph filtering on time-varying graphs",

abstract = "We have recently seen a surge of work on distributed graph filters, extending classical results to the graph setting. State of the art filters have however only been examined from a deterministic standpoint, ignoring the impact of stochasticity in the computation (e.g., temporal fluctuation of links) and input (e.g., the value of each node is a random process). Initiating the study of stochastic graph signal processing, this paper shows that a prominent class of graph filters, namely autoregressive moving average (ARMA) filters, are suitable for the stochastic setting. In particular, we prove that an ARMA filter that operates on a stochastic signal over a stochastic graph is equivalent, in the mean, to the same filter operating on the expected signal over the expected graph. We also characterize the variance of the output and we provide an upper bound for its average value among different nodes. Our results are validated by numerical simulations.",

keywords = "Steady-state, Upper bound, Laplace equations, Eigenvalues and eigenfunctions, Frequency response, Vovariance matrices, Random processes",

author = "Elvin Isufi and A Simonetto and A Loukas and G Leus",

year = "2016",

month = jan,

day = "21",

doi = "10.1109/CAMSAP.2015.7383743",

language = "English",

isbn = "978-1-4799-1963-5",

publisher = "IEEE Society",

pages = "89--92",

editor = "C Richard and F Gini",

booktitle = "Proceedings of the 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing",

note = "CAMSAP 2015, Cancun, Mexico ; Conference date: 13-12-0215 Through 16-12-2015",

}