## 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

ER -

## 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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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

ER -

## 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 -

## 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/1415},

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/1415

ER -

## Graph Frequency Analysis of Brain Signals

This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and image grids has been recently generalized to irregular graph domains, and defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions. Brain network frequency also enables the decomposition of brain signals into pieces corresponding to smooth or rapid variations.

## projectSummary_2016_IEEE.pdf

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url = {http://sigport.org/1134},

author = {Weiyu Huang; Leah Goldsberry; Nicholas F. Wymbs; Scott T. Grafton; Danielle S. Bassett; and Alejandro Ribeiro },

publisher = {IEEE SigPort},

title = {Graph Frequency Analysis of Brain Signals},

year = {2016} }

T1 - Graph Frequency Analysis of Brain Signals

AU - Weiyu Huang; Leah Goldsberry; Nicholas F. Wymbs; Scott T. Grafton; Danielle S. Bassett; and Alejandro Ribeiro

PY - 2016

PB - IEEE SigPort

UR - http://sigport.org/1134

ER -

## Graph Filter Banks With M-Channels, Maximal Decimation, and Perfect Reconstruction

Signal processing on graphs finds applications in many areas. Motivated by recent developments, this paper studies the concept of spectrum folding (aliasing) for graph signals under the downsample-then-upsample operation. In this development, we use a special eigenvector structure that is unique to the adjacency matrix of M-block cyclic matrices. We then introduce M-channel maximally decimated filter banks. Manipulating the characteristics of the aliasing effect, we construct polynomial filter banks with perfect reconstruction property.

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url = {http://sigport.org/1077},

author = {Oguzhan Teke; Palghat P. Vaidyanathan },

publisher = {IEEE SigPort},

title = {Graph Filter Banks With M-Channels, Maximal Decimation, and Perfect Reconstruction},

year = {2016} }

T1 - Graph Filter Banks With M-Channels, Maximal Decimation, and Perfect Reconstruction

AU - Oguzhan Teke; Palghat P. Vaidyanathan

PY - 2016

PB - IEEE SigPort

UR - http://sigport.org/1077

ER -

## Graph Filter Banks With M-Channels, Maximal Decimation, and Perfect Reconstruction

Signal processing on graphs finds applications in many areas. Motivated by recent developments, this paper studies the concept of spectrum folding (aliasing) for graph signals under the downsample-then-upsample operation. In this development, we use a special eigenvector structure that is unique to the adjacency matrix of M-block cyclic matrices. We then introduce M-channel maximally decimated filter banks. Manipulating the characteristics of the aliasing effect, we construct polynomial filter banks with perfect reconstruction property.

### Paper Details

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url = {http://sigport.org/1068},

author = {Oguzhan Teke; Palghat P. Vaidyanathan },

publisher = {IEEE SigPort},

title = {Graph Filter Banks With M-Channels, Maximal Decimation, and Perfect Reconstruction},

year = {2016} }

T1 - Graph Filter Banks With M-Channels, Maximal Decimation, and Perfect Reconstruction

AU - Oguzhan Teke; Palghat P. Vaidyanathan

PY - 2016

PB - IEEE SigPort

UR - http://sigport.org/1068

ER -