Pierre Vandergheynst

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What is Trending on Wikipedia? Capturing Trends and Language Biases Across Wikipedia Editions

Pierre Vandergheynst, Nicolas Aspert, Volodymyr Miz, Benjamin Ricaud, Joëlle Hanna

In this work, we propose an automatic evaluation and comparison of the browsing behavior of Wikipedia readers that can be applied to any language editions of Wikipedia. As an example, we focus on English, French, and Russian languages during the last four ...

ACM2020

A Graph-structured Dataset for Wikipedia Research

Pierre Vandergheynst, Nicolas Aspert, Volodymyr Miz, Benjamin Ricaud

Wikipedia is a rich and invaluable source of information. Its central place on the Web makes it a particularly interesting object of study for scientists. Researchers from different domains used various complex datasets related to Wikipedia to study langua ...

ACM2019

Fourier could be a data scientist: From graph Fourier transform to signal processing on graphs

Pierre Vandergheynst, Benjamin Ricaud, Nicolas Tremblay

The legacy of Joseph Fourier in science is vast, especially thanks to the essential tool that the Fourier transform is. The flexibility of this analysis, its computational efficiency and the physical interpretation it offers makes it a cornerstone in many ...

ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER2019

Tensor Robust Pca On Graphs

Pierre Vandergheynst, Nauman Shahid, Francesco Grassi

We propose a graph signal processing framework to overcome the computational burden of Tensor Robust PCA (TRPCA). Our framework also serves as a convex alternative to graph regularized tensor factorization methods. Our method is based on projecting a tenso ...

IEEE2019

Anomaly detection in the dynamics of web and social networks

Pierre Vandergheynst, Kirell Maël Benzi, Volodymyr Miz, Benjamin Ricaud

In this work, we propose a new, fast and scalable method for anomaly detection in large time-evolving graphs. It may be a static graph with dynamic node attributes (e.g. time-series), or a graph evolving in time, such as a temporal network. We define an an ...

2019

Distributed Signal Processing via Chebyshev Polynomial Approximation

Pascal Frossard, David Shuman, Pierre Vandergheynst, Daniel Kressner

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 of these operators. The proposed method features approximations of ...

2018

Spectrally approximating large graphs with smaller graphs

Pierre Vandergheynst, Andreas Loukas

How does coarsening affect the spectrum of a general graph? We provide conditions such that the principal eigenvalues and eigenspaces of a coarsened and original graph Laplacian matrices are close. The achieved approximation is shown to depend on standard ...

2018

Adaptive Graph-Based Total Variation for Tomographic Reconstructions

Pierre Vandergheynst, Nauman Shahid

IEEE Institute of Electrical and Electronics Engineers2018

Fast Approximate Spectral Clustering for Dynamic Networks

Pierre Vandergheynst, Lionel Jérémie Martin, Andreas Loukas

Spectral clustering is a widely studied problem, yet its complexity is prohibitive for dynamic graphs of even modest size. We claim that it is possible to get information from past cluster assignments to expedite computation. Our approach builds on a recen ...

2018

Graph Signal Processing: Overview, Challenges and Applications

Pascal Frossard, Pierre Vandergheynst

Research in graph signal processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper, we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing, along w ...

2018

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