Publications associées à Sequential metric dimension for random graphs

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Learning time varying graphs

Pascal Frossard, Andreas Loukas, Vasilis Kalofolias

We consider the problem of inferring the hidden structure of high-dimensional dynamic systems from the perspective of graph learning. In particular, we aim at capturing the dynamic relationships between nodes by a sequence of graphs. Our approach is motiva ...

Ieee2017

Stationary signal processing on graphs

Pierre Vandergheynst, Nathanaël Perraudin

Graphs are a central tool in machine learning and information processing as they allow to conveniently capture the structure of complex datasets. In this context, it is of high importance to develop flexible models of signals defined over graphs or network ...

Institute of Electrical and Electronics Engineers2017

Scaling a Reliable Distributed System

Alexandre David Olivier Maurer

We consider the problem of reliably connecting an arbitrarily large set of computers (nodes) with communication channels. Reliability means here the ability, for any two nodes, to remain connected (i.e., their ability to communicate) with probability at le ...

LPD EPFL2016

Independence number of generalized Petersen graphs

Javad Ebrahimi Boroojeni

Determining the size of a maximum independent set of a graph G, denoted by alpha(G), is an NP-hard problem. Therefore many attempts are made to find upper and lower bounds, or exact values of alpha(G) for special classes of graphs. This paper is aimed towa ...

Charles Babbage Res Ctr2016

Assembling a Network out of Ambiguous Patches

Matthias Grossglauser, Lyudmila Yartseva, Jefferson Elbert Simões

Many graph mining and network analysis problems rely on the availability of the full network over a set of nodes. But inferring a full network is sometimes non-trivial if the raw data is in the form of many small {\em patches} or subgraphs, of the true net ...

2016

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

Principal Patterns on Graphs: Discovering Coherent Structures in Datasets

Pierre Vandergheynst, Kirell Maël Benzi, Benjamin Ricaud

Graphs are now ubiquitous in almost every field of research. Recently, new research areas devoted to the analysis of graphs and data associated to their vertices have emerged. Focusing on dynamical processes, we propose a fast, robust and scalable framewor ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Signal-Adapted Tight Frames on Graphs

Dimitri Nestor Alice Van De Ville, Hamid Behjat

The analysis of signals on complex topologies modeled by graphs is a topic of increasing importance. Decompositions play a crucial role in the representation and processing of such information. Here, we propose a new tight frame design that is adapted to a ...

Institute of Electrical and Electronics Engineers2016

Sequential metric dimension for random graphs

Graph Chatbot

Chattez avec Graph Search

Learning time varying graphs

Exploring Selection Bias by Causal Frailty Models

Stationary signal processing on graphs

Scaling a Reliable Distributed System

Independence number of generalized Petersen graphs

Assembling a Network out of Ambiguous Patches

Disjoint edges in topological graphs and the tangled-thrackle conjecture

Principal Patterns on Graphs: Discovering Coherent Structures in Datasets

Signal-Adapted Tight Frames on Graphs

Modeling and Controlling epidemics : a graph perspective

Scaling a Reliable Distributed System

Independence number of generalized Petersen graphs

Modeling and Controlling epidemics : a graph perspective

Exploring Selection Bias by Causal Frailty Models

Stationary signal processing on graphs

Learning time varying graphs

Assembling a Network out of Ambiguous Patches

Disjoint edges in topological graphs and the tangled-thrackle conjecture

Principal Patterns on Graphs: Discovering Coherent Structures in Datasets

Signal-Adapted Tight Frames on Graphs