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Tracking Time-Vertex Propagation using Dynamic Graph Wavelets

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A Windowed Graph Fourier Transform

Pierre Vandergheynst, Benjamin Ricaud, David Shuman

The prevalence of signals on weighted graphs is increasing; however, because of the irregular structure of weighted graphs, classical signal processing techniques cannot be directly applied to signals on graphs. In this paper, we define generalized transla ...

Ieee2012

A Windowed Graph Fourier Transform

Pierre Vandergheynst, Benjamin Ricaud, David Shuman

IEEE2012

Formation preserving path finding in 3-D terrains

Ali Galip Bayrak

Navigation of a group of autonomous agents that are required to maintain a formation is a challenging task which has not been studied much especially in 3-D terrains. This paper presents a novel approach to collision free path finding of multiple agents pr ...

Springer Verlag2012

On localisation and uncertainty measures on graphs

Nathanaël Perraudin

Due to the appearance of data on networks such as internet or Facebook, the number of applications of signal on weighted graph is increasing. Unfortunately, because of the irregular structure of this data, classical signal processing techniques are not app ...

2012

Robust Non-Rigid Registration of 2D and 3D Graphs

Pascal Fua, Jan Kybic, Przemyslaw Rafal Glowacki

We present a new approach to matching graphs embedded in R2 or R3. Unlike earlier methods, our approach does not rely on the similarity of local appearance features, does not require an initial alignment, can handle partial matches, and can cope with non-l ...

2012

Space crossing numbers

We define the crossing number for an embedding of a graph G into R^3, and prove a lower bound on it which almost implies the classical crossing lemma. We also give sharp bounds on the space crossing numbers of pseudo-random graphs. ...

2011

Chebyshev polynomial approximation for transductive learning on graphs

Nathanaël Perraudin

The goal of transductive learning is to find a way to recover the labels of lots of data with only a few known samples. In this work, we will work on graphs for two reasons. First, it’s possible to construct a graph from a given dataset with features. The ...

2011

Semi-Supervised Learning with Spectral Graph Wavelets

Pierre Vandergheynst, David Shuman, Mohammadjavad Faraji

We consider the transductive learning problem when the labels belong to a continuous space. Through the use of spectral graph wavelets, we explore the benefits of multiresolution analysis on a graph constructed from the labeled and unlabeled data. The spec ...

2011

A lower bound for algebraic connectivity based on the connection-graph-stability method

Martin Hasler, Ali Ajdari Rad, Mahdi Jalili

This paper introduces the connection-graph-stability method and uses it to establish a new lower bound on the algebraic connectivity of graphs (the second smallest eigenvalue of the Laplacian matrix of the graph) that is sharper than the previously publish ...

Elsevier2011

Content Preference Estimation In Online Social Networks: Message Passing Versus Sparse Reconstruction On Graphs

We design two different strategies for computing the unknown content preferences in an online social network based on a small set of nodes in the corresponding social graph for which this information is available ahead of time. The techniques take advantag ...

2011