Publications associées à A tutorial on graph models for scheduling round-robin sports tournaments

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Slepian Guided Filtering of Graph Signals

Dimitri Nestor Alice Van De Ville, Miljan Petrovic

Joint localization of graph signals in vertex and spectral domain is achieved in Slepian vectors calculated by either maximizing energy concentration (mu) or minimizing modified embedded distance (xi) in the subgraph of interest. On the other hand, graph L ...

SPIE-INT SOC OPTICAL ENGINEERING2019

Graph Learning with Partial Observations: Role of Degree Concentration

Ali H. Sayed, Augusto José Rabelo Almeida Santos

In this work we consider the problem of learning an Erdos-Renyi graph over a diffusion network when: i) data from only a limited subset of nodes are available (partial observation); ii) and the inferential goal is to discover the graph of interconnections ...

IEEE2019

GOT: An Optimal Transport framework for Graph comparison

Pascal Frossard, Mireille El Gheche, Hermina Petric Maretic, Giovanni Chierchia

We present a novel framework based on optimal transport for the challenging problem of comparing graphs. Specifically, we exploit the probabilistic distribution of smooth graph signals defined with respect to the graph topology. This allows us to derive an ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Spectral MAB for Unknown Graph Processes

Pascal Frossard, Laura Toni

In this work, we study graph-based multi-arms bandit (MAB) problems aimed at optimizing actions on irregular and high-dimensional graphs. More formally, we consider a decision-maker that takes sequential actions over time and observes the experienced rewar ...

IEEE Computer Society2018

The Sketching Complexity of Graph and Hypergraph Counting

Mikhail Kapralov, John Michael Goddard Kallaugher

Subgraph counting is a fundamental primitive in graph processing, with applications in social network analysis (e.g., estimating the clustering coefficient of a graph), database processing and other areas. The space complexity of subgraph counting has been ...

IEEE COMPUTER SOC2018

Geometric deep learning: going beyond Euclidean data

Pierre Vandergheynst, Michael Bronstein

Many signal processing problems involve data whose underlying structure is non-Euclidean, but may be modeled as a manifold or (combinatorial) graph. For instance, in social networks, the characteristics of users can be modeled as signals on the vertices of ...

2017

Filtering Random Graph Processes Over Random Time-Varying Graphs

Andreas Loukas

Graph filters play a key role in processing the graph spectra of signals supported on the vertices of a graph. However, despite their widespread use, graph filters have been analyzed only in the deterministic setting, ignoring the impact of stochasticity i ...

Institute of Electrical and Electronics Engineers2017

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

A tutorial on graph models for scheduling round-robin sports tournaments

Graph Chatbot

Chattez avec Graph Search

Slepian Guided Filtering of Graph Signals

Graph Learning with Partial Observations: Role of Degree Concentration

GOT: An Optimal Transport framework for Graph comparison

Apartment Structure Estimation Using Fully Convolutional Networks and Graph Model

Spectral MAB for Unknown Graph Processes

The Sketching Complexity of Graph and Hypergraph Counting

Robust and Efficient Data Clustering with Signal Processing on Graphs

Geometric deep learning: going beyond Euclidean data

Filtering Random Graph Processes Over Random Time-Varying Graphs

Assembling a Network out of Ambiguous Patches

Filtering Random Graph Processes Over Random Time-Varying Graphs

Geometric deep learning: going beyond Euclidean data

GOT: An Optimal Transport framework for Graph comparison

Graph Learning with Partial Observations: Role of Degree Concentration

The Sketching Complexity of Graph and Hypergraph Counting

Robust and Efficient Data Clustering with Signal Processing on Graphs

Assembling a Network out of Ambiguous Patches

Slepian Guided Filtering of Graph Signals

Apartment Structure Estimation Using Fully Convolutional Networks and Graph Model

Spectral MAB for Unknown Graph Processes