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Efficient large-scale graph processing: optimisations for storage, performance and evolving graphs

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Disjoint edges in topological graphs and the tangled-thrackle conjecture

Andres Jacinto Ruiz Vargas

It is shown that fora constant t is an element of N, every simple topological graph on n vertices has 0(n) edges if the graph has no two sets of t edges such that every edge in one set is disjoint from all edges of the other set (i.e., the complement of th ...

Academic Press Ltd- Elsevier Science Ltd2016

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Weighted undirected graphs are a simple, yet powerful way to encode structure in data. A first question we need to address regarding such graphs is how to use them effectively to enhance machine learning problems. A second but more important question is ho ...

EPFL2016

Infectious Disease Containment Based on a Wireless Sensor System

Marcel Salathé, Xiao Sun

Infectious diseases pose a serious threat to public health due to its high infectivity and potentially high mortality. One of the most effective ways to protect people from being infected by these diseases is through vaccination. However, due to various re ...

Ieee-Inst Electrical Electronics Engineers Inc2016

How to learn a graph from smooth signals

Vasilis Kalofolias

We propose a framework that learns the graph structure underlying a set of smooth signals. Given a real m by n matrix X whose rows reside on the vertices of an unknown graph, we learn the edge weights w under the smoothness assumption that trace(X^TLX) is ...

2016

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

Chaos: Scale-out Graph Processing from Secondary Storage

Willy Zwaenepoel, Laurent Bindschaedler, Amitabha Roy, Jasmina Malicevic

Chaos scales graph processing from secondary storage to multiple machines in a cluster. Earlier systems that process graphs from secondary storage are restricted to a single ma- chine, and therefore limited by the bandwidth and capacity of the storage syst ...

2015

Modeling and Controlling epidemics : a graph perspective

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The social and economical impact a virus has in a population is not negligible. For this reason, gaining a theoretical understanding of the behavior of a virus is of prime importance. This project is going to be divided in several parts. First, we will dis ...

2015

Graph-based denoising for time-varying point clouds

Pierre Vandergheynst, Johann Paratte, Yann Mikaël Schoenenberger

Noisy 3D point clouds arise in many applications. They may be due to errors when creating a 3D model from images or simply to imprecise depth sensors. Point clouds can be given geometrical structure using graphs created from the similarity information betw ...

2015

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling

k

-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, \ie, independent of the graph structure, and its perform ...

2015

Reconstruction of Images from Gabor Graphs with Applications in Facial Image Processing

Graphs labeled with complex-valued Gabor jets are one of the important data formats for face recognition and the classification of facial images into medically relevant classes like genetic syndromes. We here present an interpolation rule and an iterative ...

2015

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling

k

2015