Publications associées à Graph Coarsening with Preserved Spectral Properties

Everything You Always Wanted to Know about Multicore Graph Processing but Were Afraid to Ask

Willy Zwaenepoel, Baptiste Joseph Eustache Lepers, Jasmina Malicevic

Graph processing systems are used in a wide variety of fields, ranging from biology to social networks, and a large number of such systems have been described in the recent literature. We perform a systematic comparison of various techniques proposed to sp ...

USENIX Association2017

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Pierre Vandergheynst, Xavier Bresson, Michaël Defferrard

In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or wor ...

2016

Source Localization on Graphs via l1 Recovery and Spectral Graph Theory

Pierre Vandergheynst, Xavier Bresson, Rodrigo Cerqueira Gonzalez Pena

We cast the problem of source localization on graphs as the simultaneous problem of sparse recovery and diffusion ker- nel learning. An l1 regularization term enforces the sparsity constraint while we recover the sources of diffusion from a single snapshot ...

Ieee2016

Multilinear Low-Rank Tensors on Graphs & Applications

Pierre Vandergheynst, Nauman Shahid, Francesco Grassi

We propose a new framework for the analysis of low- rank tensors which lies at the intersection of spectral graph theory and signal processing. As a first step, we present a new graph based low-rank decomposition which approximates the classical low-rank S ...

2016

Graph-based Image Inpainting

Michaël Defferrard

The project goal was to explore the applications of spectral graph theory to address the inpainting problem of large missing chunks. We used a non-local patch graph representation of the image and proposed a structure detector which leverages the graph rep ...

2014

A Bayesian Method for Matching Two Similar Graphs without Seeds

Matthias Grossglauser, Pedram Pedarsani

Approximate graph matching (AGM) refers to the problem of mapping the vertices of two structurally similar graphs, which has applications in social networks, computer vision, chemistry, and biology. Given its computational cost, AGM has mostly been limited ...

2013

Graph-Constrained Group Testing

Amin Karbasi, Mahdi Cheraghchi Bashi Astaneh, Soheil Mohajerzefreh

Non-adaptive group testing involves grouping arbitrary subsets of

n

items into different pools. Each pool is then tested and defective items are identified. A fundamental question involves minimizing the number of pools required to identify at most

d

d ...

2012

Finite graphs and amenability

Gábor Elek

Hyperfiniteness or amenability of measurable equivalence relations and group actions has been studied for almost fifty years. Recently, unexpected applications of hyperfiniteness were found in computer science in the context of testability of graph propert ...

Academic Press Inc Elsevier Science2012

Wavelets on graphs via spectral graph theory

Pierre Vandergheynst, Rémi Gribonval

We propose a novel method for constructing wavelet transforms of functions defined on the vertices of an arbitrary finite weighted graph. Our approach is based on defining scaling using the the graph analogue of the Fourier domain, namely the spectral deco ...

Elsevier2011

Graph-Constrained Group Testing

Amin Karbasi, Mahdi Cheraghchi Bashi Astaneh, Soheil Mohajerzefreh

Non-adaptive group testing involves grouping arbitrary subsets of

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items into different pools and identifying defective items based on tests obtained for each pool. Motivated by applications in network tomography, sensor networks and infection propagati ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Graph Coarsening with Preserved Spectral Properties

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Everything You Always Wanted to Know about Multicore Graph Processing but Were Afraid to Ask

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Source Localization on Graphs via l1 Recovery and Spectral Graph Theory

Multilinear Low-Rank Tensors on Graphs & Applications

Graph-based Image Inpainting

A Bayesian Method for Matching Two Similar Graphs without Seeds

Graph-Constrained Group Testing

Finite graphs and amenability

Wavelets on graphs via spectral graph theory

Graph-Constrained Group Testing

Everything You Always Wanted to Know about Multicore Graph Processing but Were Afraid to Ask

Finite graphs and amenability

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Graph-Constrained Group Testing

Source Localization on Graphs via l1 Recovery and Spectral Graph Theory

Multilinear Low-Rank Tensors on Graphs & Applications

Graph-Constrained Group Testing

Graph-based Image Inpainting

A Bayesian Method for Matching Two Similar Graphs without Seeds

Wavelets on graphs via spectral graph theory