## MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING

In this paper, we discuss the problem of modeling a graph signal on a directed graph when observing only partially the graph signal. The graph signal is recovered using a learned graph filter. The novelty is to use the random walk operator associated to an ergodic random walk on the graph, so as to define and learn a graph filter, expressed as a polynomial of this operator. Through the study of different cases, we show the efficiency of the signal modeling using the random walk operator compared to existing methods using the adjacency matrix or ignoring the directions in the graph.

## Borgnat_talk_GlobalSIP_2018.pdf

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url = {http://sigport.org/3812},

author = {Harry Sevi; Gabriel Rilling; Pierre Borgnat },

publisher = {IEEE SigPort},

title = {MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING},

year = {2018} }

T1 - MODELING SIGNALS OVER DIRECTED GRAPHS THROUGH FILTERING

AU - Harry Sevi; Gabriel Rilling; Pierre Borgnat

PY - 2018

PB - IEEE SigPort

UR - http://sigport.org/3812

ER -

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- 8 December 2018 - 1:41pm
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url = {http://sigport.org/3802},

author = {Madeleine S. Kotzagiannidis; Mike E. Davies },

publisher = {IEEE SigPort},

title = {Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs},

year = {2018} }

T1 - Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs

AU - Madeleine S. Kotzagiannidis; Mike E. Davies

PY - 2018

PB - IEEE SigPort

UR - http://sigport.org/3802

ER -

## Predicting Power Outages Using Graph Neural Networks

Power outages have a major impact on economic development due to the dependence of (virtually all) productive sectors on electric power. Thus, many resources within the scientific and engineering communities have been employed to improve the efficiency and reliability of power grids. In particular, we consider the problem of predicting power outages based on the current weather conditions. Weather measurements taken by a sensor network naturally fit within the graph signal processing framework since the measurements are related by the relative position of the sensors.

## globalsip_2018_poster.pdf

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url = {http://sigport.org/3800},

author = {Damian Owerko; Fernando Gama; Alejandro Ribeiro },

publisher = {IEEE SigPort},

title = {Predicting Power Outages Using Graph Neural Networks},

year = {2018} }

T1 - Predicting Power Outages Using Graph Neural Networks

AU - Damian Owerko; Fernando Gama; Alejandro Ribeiro

PY - 2018

PB - IEEE SigPort

UR - http://sigport.org/3800

ER -

## Rumour Source Detection in Social Networks using Partial Observations

## globalsip18.pdf

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url = {http://sigport.org/3716},

author = { },

publisher = {IEEE SigPort},

title = {Rumour Source Detection in Social Networks using Partial Observations},

year = {2018} }

T1 - Rumour Source Detection in Social Networks using Partial Observations

AU -

PY - 2018

PB - IEEE SigPort

UR - http://sigport.org/3716

ER -

## Convolutional Neural Networks via Node-Varying Graph Filters

Convolutional neural networks (CNNs) are being applied to an increasing number of problems and fields due to their superior performance in classification and regression tasks. Since two of the key operations that CNNs implement are convolution and pooling, this type of networks is implicitly designed to act on data described by regular structures such as images. Motivated by the recent interest in processing signals defined in irregular domains, we advocate a CNN architecture that operates on signals supported on graphs.

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- 31 May 2018 - 7:03pm
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url = {http://sigport.org/3226},

author = {Fernando Gama; Geert Leus; Antonio Marques; Alejandro Ribeiro },

publisher = {IEEE SigPort},

title = {Convolutional Neural Networks via Node-Varying Graph Filters},

year = {2018} }

T1 - Convolutional Neural Networks via Node-Varying Graph Filters

AU - Fernando Gama; Geert Leus; Antonio Marques; Alejandro Ribeiro

PY - 2018

PB - IEEE SigPort

UR - http://sigport.org/3226

ER -

## Control of Graph Signals over Random Time-Varying Graphs

In this work, we jointly exploit tools from graph signal processing and control theory to drive a bandlimited graph signal that is being diffused on a random time-varying graph from a subset of nodes. As our main contribution, we rely only on the statistics of the graph to introduce the concept of controllability in the mean, and therefore drive the signal on the expected graph to a desired state.

## control-icassp18-poster.pdf

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url = {http://sigport.org/3041},

author = {Fernando Gama; Elvin Isufi; Geert Leus and Alejandro Ribeiro },

publisher = {IEEE SigPort},

title = {Control of Graph Signals over Random Time-Varying Graphs},

year = {2018} }

T1 - Control of Graph Signals over Random Time-Varying Graphs

AU - Fernando Gama; Elvin Isufi; Geert Leus and Alejandro Ribeiro

PY - 2018

PB - IEEE SigPort

UR - http://sigport.org/3041

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## Demixing and blind deconvolution of graph-diffused signals

We extend the classical joint problem of signal demixing, blind deconvolution,

and filter identification to the realm of graphs. The model is that

each mixing signal is generated by a sparse input diffused via a graph filter.

Then, the sum of diffused signals is observed. We identify and address

two problems: 1) each sparse input is diffused in a different graph; and 2)

all signals are diffused in the same graph. These tasks amount to finding

the collections of sources and filter coefficients producing the observation.

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- 19 April 2018 - 4:51pm
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url = {http://sigport.org/3027},

author = {Fernando J. Iglesias; Santiago Segarra; Samuel Rey-Escudero; Antonio G. Marques; David Ramirez },

publisher = {IEEE SigPort},

title = {Demixing and blind deconvolution of graph-diffused signals},

year = {2018} }

T1 - Demixing and blind deconvolution of graph-diffused signals

AU - Fernando J. Iglesias; Santiago Segarra; Samuel Rey-Escudero; Antonio G. Marques; David Ramirez

PY - 2018

PB - IEEE SigPort

UR - http://sigport.org/3027

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## Weak Law of Large Numbers for Stationary Graph Processes

The ability to obtain accurate estimators from a set of measurements is a key factor in science and engineering. Typically, there is an inherent assumption that the measurements were taken in a sequential order, be it in space or time. However, data is increasingly irregular so this assumption of sequentially obtained measurements no longer holds. By leveraging notions of graph signal processing to account for these irregular domains, we propose an unbiased estimator for the mean of a wide sense stationary graph process based on the diffusion of a single realization.

## glln-icassp17-poster.pdf

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url = {http://sigport.org/1585},

author = {Fernando Gama; Alejandro Ribeiro },

publisher = {IEEE SigPort},

title = {Weak Law of Large Numbers for Stationary Graph Processes},

year = {2017} }

T1 - Weak Law of Large Numbers for Stationary Graph Processes

AU - Fernando Gama; Alejandro Ribeiro

PY - 2017

PB - IEEE SigPort

UR - http://sigport.org/1585

ER -

## Tracking Time-Vertex Propagation using Dynamic Graph Wavelets

Graph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph. So far, the research efforts have been focused on static graph signals. However numerous applications involve graph signals evolving in time, such as spreading or propagation of waves on a network. The analysis of this type of data requires a new set of methods that takes into account the time and graph dimensions. We propose a novel class of wavelet frames named Dynamic Graph Wavelets, whose time-vertex evolution follows a dynamic process.

## globalsip_grassi.pdf

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url = {http://sigport.org/1428},

author = {Francesco Grassi; Nathanael Perraudin; Benjamin Ricaud },

publisher = {IEEE SigPort},

title = {Tracking Time-Vertex Propagation using Dynamic Graph Wavelets},

year = {2016} }

T1 - Tracking Time-Vertex Propagation using Dynamic Graph Wavelets

AU - Francesco Grassi; Nathanael Perraudin; Benjamin Ricaud

PY - 2016

PB - IEEE SigPort

UR - http://sigport.org/1428

ER -

## Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems

Sampling and reconstruction of bandlimited graph signals have well-appreciated merits for dimensionality reduction, affordable storage, and online processing of streaming network data. However, these parsimonious signals are oftentimes encountered with high-dimensional linear inverse problems. Hence, interest shifts from reconstructing the signal itself towards instead approximating the input to a prescribed linear operator efficiently.

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url = {http://sigport.org/1420},

author = {Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro },

publisher = {IEEE SigPort},

title = {Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems},

year = {2016} }

T1 - Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems

AU - Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro

PY - 2016

PB - IEEE SigPort

UR - http://sigport.org/1420

ER -