Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

We study the optimal design of graph filters (GFs) to implement arbitrary linear
transformations between graph signals. GFs can be represented by matrix polynomials of …

One of the cornerstones of the field of signal processing on graphs are graph filters, direct
analogs of classical filters, but intended for signals defined on graphs. This paper brings …

Stationarity is a cornerstone property that facilitates the analysis and processing of random
signals in the time domain. Although time-varying signals are abundant in nature, in many …

Graph filters are one of the core tools in graph signal processing. A central aspect of them is
their direct distributed implementation. However, the filtering performance is often traded …

Unions of graph multiplier operators are an important class of linear operators for processing
signals defined on graphs. We present a novel method to efficiently distribute the application …

Many of the algorithms that have been proposed in the field of distributed computation rely
on assumptions that require nodes to be aware of some global parameters. In this paper, we …

We introduce the concept of autoregressive moving average (ARMA) filters on a graph and
show how they can be implemented in a distributed fashion. Our graph filter design …

Network processes are often represented as signals defined on the vertices of a graph. To
untangle the latent structure of such signals, one can view them as outputs of linear graph …

Many signal processing problems in wireless sensor networks can be solved by graph
filtering techniques. Finite impulse response (FIR) graph filters (GFs) have received more …