Concept

Bridge (graph theory)

Related publications (23)

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation

In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Perturbation (GAP), which adds stochastic noise to the GNN's aggregation functio ...

Usenix Assoc2023

Graph-based image representation learning

Renata Khasanova

Though deep learning (DL) algorithms are very powerful for image processing tasks, they generally require a lot of data to reach their full potential. Furthermore, there is no straightforward way to impose various properties, given by the prior knowledge a ...

EPFL2019

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

Consistency of Cheeger and Ratio Graph Cuts

Xavier Bresson

This paper establishes the consistency of a family of graph-cut-based algorithms for clustering of data clouds. We consider point clouds obtained as samples of a ground-truth measure. We investigate approaches to clustering based on minimizing objective fu ...

Microtome Publishing2016

Segmentation of Developing Human Embryo in Time-lapse Microscopy

Mathieu Salzmann

Being able to efficiently segment a developing embryo from background clutter constitutes an important step in automated monitoring of human embryonic cells. State-of-the-art automatic segmentation methods remain ill-suited to handle the complex behavior a ...

IEEE2016

Downsampling of Signals on Graphs Via Maximum Spanning Trees

Quy Ha Nguyen, Minh Do

Downsampling of signals living on a general weighted graph is not as trivial as of regular signals where we can simply keep every other samples. In this paper we propose a simple, yet effective downsampling scheme in which the underlying graph is approxima ...

Ieee-Inst Electrical Electronics Engineers Inc2015

Hierarchical Routing Over Dynamic Wireless Networks

Suhas Diggavi, Matthias Grossglauser

The topology of a mobile wireless network changes over time. Maintaining routes between all nodes requires the continuous transmission of control information, which consumes precious power and bandwidth resources. Many routing protocols have been developed ...

Wiley-Blackwell2015

Free Edge Lengths in Plane Graphs

Radoslav Fulek, Filip Moric

We study the impact of metric constraints on the realizability of planar graphs. Let G be a subgraph of a planar graph H (where H is the "host" of G). The graph G is free in H if for every choice of positive lengths for the edges of G, the host H has a pla ...

Springer2015

Fast Partitioning of Vector-Valued Images

Martin Kurt Storath

We propose a fast splitting approach to the classical variational formulation of the image partitioning problem, which is frequently referred to as the Potts or piecewise constant Mumford-Shah model. For vector-valued images, our approach is significantly ...

Siam Publications2014

Unsupervised Texture Segmentation Using Monogenic Curvelets and the Potts Model

Michaël Unser, Martin Kurt Storath

We present a method for the unsupervised segmentation of textured images using Potts functionals, which are a piecewise-constant variant of the Mumford and Shah functionals. We propose a minimization strategy based on the alternating direction method of mu ...

IEEE2014

Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets

Pascal Fua, Aurélien Lucchi, Yunpeng Li

We propose a working set based approximate subgradient descent algorithm to minimize the margin-sensitive hinge loss arising from the soft constraints in max-margin learning frameworks, such as the structured SVM. We focus on the setting of general graphic ...

2013

Audio-Visual Object Extraction using Graph Cuts

Pierre Vandergheynst, Anna Llagostera Casanovas

We propose a novel method to automatically extract the audio-visual objects that are present in a scene. First, the synchrony between related events in audio and video channels is exploited to identify the possible locations of the sound sources. Video reg ...

Institute of Electrical and Electronics Engineers2012

Graph Transformations preserving the stability number

Dominique de Werra

We analyze the relations between several graph transformations that were introduced to be used in procedures determining the stability number of a graph. We show that all these transformations can be decomposed into a sequence of edge deletions and twin de ...

Elsevier Science Bv2012

Intersection Patterns of Edges in Topological Graphs

Radoslav Fulek

This thesis is devoted to crossing patterns of edges in topological graphs. We consider the following four problems: A thrackle is a graph drawn in the plane such that every pair of edges meet exactly once: either at a common endpoint or in a proper crossi ...

EPFL2012

Multi-Graph Sampling Of Online Communities Via Mean Hitting Time

We derive a framework for sampling online communities based on the mean hitting time of its members, considering that there are multiple graphs associated with the same vertex set V representing the social network. First, we formulate random walk models on ...

Ieee2012

Image Segmentation With Background Correction Using A Multiplicative Smoothing-Spline Model

Michaël Unser, Aurélien Bourquard, Ramtin Madani

This paper presents an image-segmentation method which compensates multiplicative distortions based on smooth regularity assumptions. In this work, we generalize the original Chan-Vese functional to handle a continuous multiplicative bias. In the derivatio ...

Ieee2012

Fast Computation of Small Cuts via Cycle Space Sampling

David Pritchard

We describe a new sampling-based method to determine cuts in an undirected graph. For a graph (V, E), its cycle space is the family of all subsets of E that have even degree at each vertex. We prove that with high probability, sampling the cycle space iden ...

2011

Beyond Graphs: A New Synthesis

Dario Floreano, Claudio Mattiussi, Daniel Marbach, Peter Dürr

Artiﬁcial neural networks, electronic circuits, and gene networks are some examples of systems that can be modeled as networks, that is, as collections of interconnected nodes. In this paper we introduce the concept of terminal graph (t-graph for short), w ...

2011

Finding Objects of Interest in Images using Saliency and Superpixels

Radhakrishna Achanta

The ability to automatically find objects of interest in images is useful in the areas of compression, indexing and retrieval, re-targeting, and so on. There are two classes of such algorithms – those that find any object of interest with no prior knowledg ...

EPFL2011

k-Edge-Connectivity: Approximation and LP Relaxation

David Pritchard

This paper's focus is the following family of problems, denoted k-ECSS, where k denotes a positive integer: given a graph (V, E) and costs for each edge, find a minimum-cost subset F of E such that (V, F) is k-edge-connected. For k=1 it is the spanning tre ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2011

Related publications (23)