Concept

Connectivity (graph theory)

Publications associées (199)

Tomography of Large Adaptive Networks under the Dense Latent Regime

Ali H. Sayed, Augusto José Rabelo Almeida Santos

This work examines the problem of graph learning over a diffusion network when measurements can only be gathered from a limited fraction of agents (latent regime). Under this selling, most works in the literature rely on a degree of sparsity to provide gua ...

IEEE2018

Approximating Steiner trees and forests with minimum number of Steiner points

Nachshon Cohen

Let R be a finite set of terminals in a convex metric space (M, d). We give approximation algorithms for problems of finding a minimum size set S subset of M of additional points such that the unit-disc graph G[R boolean OR S] of R boolean OR S satisfies s ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2018

Slow dynamics in structured neural network models

Samuel Pavio Muscinelli

Humans and some other animals are able to perform tasks that require coordination of movements across multiple temporal scales, ranging from hundreds of milliseconds to several seconds. The fast timescale at which neurons naturally operate, on the order of ...

EPFL2018

A design method for distributed luenberger observers

Colin Neil Jones, Francisco Fernandes Castro Rego

The paper addresses the problem of designing distributed observers for discrete linear time-invariant (LTI) systems with distributed sensor nodes subjected to bounded measurement noise. A solution is proposed in terms of a distributed LTI Luenberger observ ...

IEEE2018

Belief control strategies for interactions over weak graphs

Ali H. Sayed

In diffusion social learning over weakly-connected graphs, it has been shown that influential agents end up shaping the beliefs of non-influential agents. In this paper, we analyse this control mechanism more closely and reveal some critical properties. In ...

IEEE2017

Social Learning Over Weakly Connected Graphs

Ali H. Sayed, Bicheng Ying

In this paper, we study diffusion social learning over weakly connected graphs. We show that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations. Under some circumstances that we clarif ...

2017

Analytical transport network theory to guide the design of 3-D microstructural networks in energy materials: Part 1. Flow without reactions

Arata Nakajo

We present a fully analytical, heuristic model the "Analytical Transport Network Model" for steady-state, diffusive, potential flow through a 3-D network. Employing a combination of graph theory, linear algebra, and geometry, the model explicitly relates a ...

Elsevier Science Bv2017

GDSP: A graphical perspective on the distributed storage systems

Michael Christoph Gastpar, Saeid Sahraei

The classical distributed storage problem can be modeled by a k-uniform complete hyper-graph where vertices represent servers and hyper-edges represent users. Hence each hyper-edge should be able to recover the full file using only the memories of the vert ...

2017

Graph-based structures in data science

Nathanaël Perraudin

State-of-the-art data analysis tools have to deal with high-dimensional data. Fortunately, the inherent dimensionality of data is often much smaller, as it has an internal structure limiting its degrees of freedom. In most cases, this structure can be appr ...

EPFL2017

Multi-level Logic Benchmarks: An Exactness Study

Giovanni De Micheli, Mathias Soeken, Pierre-Emmanuel Julien Marc Gaillardon, Luca Gaetano Amarù, Eleonora Testa, Winston Jason Haaswijk

In this paper, we study exact multi-level logic benchmarks. We refer to an exact logic benchmark, or exact benchmark in short, as the optimal implementation of a given Boolean function, in terms of minimum number of logic levels and/or nodes. Exact benchma ...

Ieee2017

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search