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Adaptive majority problems for restricted query graphs and for weighted sets

Publications associées (121)

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New Notions and Constructions of Sparsification for Graphs and Hypergraphs

Ola Nils Anders Svensson

A sparsifier of a graph G (Bencztir and Karger; Spielman and Teng) is a sparse weighted subgraph (G) over tilde that approximately retains the same cut structure of G. For general graphs, non-trivial sparsification is possible only by using weighted graphs ...

IEEE COMPUTER SOC2019

Time-resolved analysis of dynamic graphs: an extended Slepian design

Dimitri Nestor Alice Van De Ville, Raphaël Pierre Liégeois, Ibrahim Merad

Graphs are extensively used to represent networked data. In many applications, especially when considering large datasets, it is a desirable feature to focus the analysis onto specific subgraphs of interest. Slepian theory and its extension to graphs allow ...

SPIE-INT SOC OPTICAL ENGINEERING2019

On the Rainbow Turan number of paths

Abhishek Methuku

Let F be a fixed graph. The rainbow Turan number of F is defined as the maximum number of edges in a graph on n vertices that has a proper edge-coloring with no rainbow copy of F (i.e., a copy of F all of whose edges have different colours). The systematic ...

ELECTRONIC JOURNAL OF COMBINATORICS2019

Subadditivity Beyond Trees and the Chi-Squared Mutual Information

Emmanuel Abbé, Enric Boix Adsera

Evans et al. [1] proved the subadditivity of the mutual information in the broadcasting on tree model with binary vertex labels and symmetric edge channels. They raised the question of whether such subadditivity extends to loopy graphs in some appropriate ...

IEEE2019

Thrackles: An improved upper bound

János Pach, Radoslav Fulek

A thrackle is a graph drawn in the plane so that every pair of its edges meet exactly once: either at a common end vertex or in a proper crossing. We prove that any thrackle of n vertices has at most 1.3984n edges. Quasi-thrackles are defined similarly, ex ...

ELSEVIER SCIENCE BV2019

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

Distributed Diffusion Adaptation Over Graph Signals

Ali H. Sayed, Jie Chen, Roula Nassif

Most works on graph signal processing assume static graph signals, which is a limitation even in comparison to traditional DSP techniques where signals are modeled as sequences that evolve over time. For broader applicability, it is necessary to develop te ...

IEEE2018

Deep learning on graph for semantic segmentation of point cloud

Alexandre Thierry Robert Cherqui

This master thesis provides in-depth explanations of how deep learning and graph theory can be used together to perform pointwise classification in 3D point clouds obtained by combinations of geospatial images. That scene understanding problem arises in a ...

2018

A Novel Method for Sampling Bandlimited Graph Signals

Pascal Frossard, Pinar Akyazi, Dion Eustathios Olivier Tzamarias

In this paper we propose a novel vertex based sampling method for k-bandlimited signals lying on arbitrary graphs, that has a reasonable computational complexity and results in low reconstruction error. Our goal is to find the smallest set of vertices that ...

IEEE COMPUTER SOC2018

Graph-based Methods for Visualization and Clustering

Johann Paratte

The amount of data that we produce and consume is larger than it has been at any point in the history of mankind, and it keeps growing exponentially. All this information, gathered in overwhelming volumes, often comes with two problematic characteristics: ...

EPFL2017