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Multi-Graph Sampling Of Online Communities Via Mean Hitting Time

Publications associées (59)

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On the Turan number of ordered forests

Gábor Tardos, Istvan Tomon, Dániel József Korándi

An ordered graph H is a simple graph with a linear order on its vertex set. The corresponding Turan problem, first studied by Pach and Tardos, asks for the maximum number ex(

ACADEMIC PRESS INC ELSEVIER SCIENCE2019

Graph Laplacians for Rotation Equivariant Convolutional Neural Networks

Martino Milani

A fundamental problem in signal processing is to design computationally efficient algorithms to filter signals. In many applications, the signals to filter lie on a sphere. Meaningful examples of data of this kind are weather data on the Earth, or images o ...

2019

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

A note on discrete lattice-periodic sets with an application to Archimedean tilings

Matthias Schymura

Cao and Yuan obtained a Blichfeldt-type result for the vertex set of the edge-to-edge tiling of the plane by regular hexagons. Observing that the vertex set of every Archimedean tiling is the union of translates of a fixed lattice, we take a more general v ...

SPRINGER HEIDELBERG2019

The Sketching Complexity of Graph and Hypergraph Counting

Mikhail Kapralov, John Michael Goddard Kallaugher

Subgraph counting is a fundamental primitive in graph processing, with applications in social network analysis (e.g., estimating the clustering coefficient of a graph), database processing and other areas. The space complexity of subgraph counting has been ...

IEEE COMPUTER SOC2018

When Stuck, Flip a Coin

Slobodan Mitrovic

Many modern services need to routinely perform tasks on a large scale. This prompts us to consider the following question: How can we design efficient algorithms for large-scale computation? In this thesis, we focus on devising a general strategy to addr ...

EPFL2018

Robust and Efficient Data Clustering with Signal Processing on Graphs

Lionel Jérémie Martin

Data is pervasive in today's world and has actually been for quite some time. With the increasing volume of data to process, there is a need for faster and at least as accurate techniques than what we already have. In particular, the last decade recorded t ...

EPFL2018

Graph Heat Mixture Model Learning

Pascal Frossard, Mireille El Gheche, Hermina Petric Maretic

Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis. However, most of the available state-of-the-art methods focus on scenarios where all avai ...

IEEE2018

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

Monochromatic cycle covers in random graphs

Dániel József Korándi

A classic result of Erdos, Gyarfas and Pyber states that for every coloring of the edges of K-n with r colors, there is a cover of its vertex set by at most f(r)=O(r2logr) vertex-disjoint monochromatic cycles. In particular, the minimum number of such cove ...

WILEY2018