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Publications associées (29)

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Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion

Benjamin Ricaud, Nicolas Aspert, Volodymyr Miz

Studying real-world networks such as social networks or web networks is a challenge. These networks often combine a complex, highly connected structure together with a large size. We propose a new approach for large scale networks that is able to automatic ...

2020

In vitro investigations of red blood cell phase separation in a complex microchannel network

Francesco Clavica

Microvascular networks feature a complex topology with multiple bifurcating vessels. Nonuniform partitioning (phase separation) of red blood cells (RBCs) occurs at diverging bifurcations, leading to a heterogeneous RBC distribution that ultimately affects ...

2020

Scale-dependent measure of network centrality from diffusion dynamics

Alexis Arnaudon

Classic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection between diffusion and geometry to introduce a multiscale centrality measure. A n ...

AMER PHYSICAL SOC2020

Adaptation And Learning In Multi-Task Decision Systems

Ali H. Sayed

Adaptation and learning over multi-agent networks is a topic of great relevance with important implications. Elaborating on previous works on single-task networks engaged in decision problems, here we consider the multi-task version in the challenging scen ...

IEEE2020

Inkjet Printing of Complex Soft Machines with Densely Integrated Electrostatic Actuators

Herbert Shea, Samuel Rosset, Samuel Schlatter, Giulio Grasso

A multimaterial inkjet printing method for integrated soft multifunctional machines is reported, combining dense arrays of electrostatic actuators, multilayer electrical routing, and complex networks of microfluidic channels in one printing process. Most a ...

2020

Community-Aware Graph Signal Processing: Modularity Defines New Ways of Processing Graph Signals

Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton, Raphaël Pierre Liégeois, Miljan Petrovic

The emerging field of graph signal processing (GSP) allows one to transpose classical signal processing operations (e.g., filtering) to signals on graphs. The GSP framework is generally built upon the graph Laplacian, which plays a crucial role in studying ...

2020

Graph Signal Processing for Machine Learning: A Review and New Perspectives

Pascal Frossard, Ntorina Thanou, Laura Toni, Xiaowen Dong, Michael Bronstein

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains, such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Global robustness versus local vulnerabilities in complex synchronous networks

Philippe René Joseph Jacquod

In complex network-coupled dynamical systems, two questions of central importance are how to identify the most vulnerable components and how to devise a network making the overall system more robust to external perturbations. To address these two questions ...

2019

The key player problem in complex oscillator networks and electric power grids: Resistance centralities identify local vulnerabilities

Philippe René Joseph Jacquod, Melvyn Sandy Tyloo

Identifying key players in coupled individual systems is a fundamental problem in network theory. We investigate synchronizable network-coupled dynamical systems such as high-voltage electric power grids and coupled oscillators on complex networks. We defi ...

2019