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Concept

Giant component

Publications associées (15)

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

Ali H. Sayed

This work addresses the problem of learning the topology of a network from the signals emitted by the network nodes. These signals are generated over time through a linear diffusion process, where neighboring nodes exchange messages according to the underl ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

The Role of Adaptivity in Source Identification with Time Queries

Gergely Odor

Understanding epidemic propagation in large networks is an important but challenging task, especially since we usually lack information, and the information that we have is often counter-intuitive. An illustrative example is the dependence of the final siz ...

EPFL2022

Learning Bollobas-Riordan Graphs Under Partial Observability

Ali H. Sayed

This work examines the problem of learning the topology of a network (graph learning) from the signals produced at a subset of the network nodes (partial observability). This challenging problem was recently tackled assuming that the topology is drawn acco ...

IEEE2021

Sequential metric dimension for random graphs

Patrick Thiran, Gergely Odor

In the localization game on a graph, the goal is to find a fixed but unknown target node v* with the least number of distance queries possible. In the j-th step of the game, the player queries a single node v_j and receives, as an answer to their query, th ...

2021

Local Tomography of Large Networks Under the Low-Observability Regime

Ali H. Sayed, Augusto José Rabelo Almeida Santos

This article studies the problem of reconstructing the topology of a network of interacting agents via observations of the state-evolution of the agents. We focus on the large-scale network setting with the additional constraint of partial observations, wh ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

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

Consistent Tomography over Diffusion Networks under the Low-Observability Regime

Ali H. Sayed, Augusto José Rabelo Almeida Santos

This work considers a diffusion network responding to streaming data, and studies the problem of identifying the topology of a subnetwork of observable agents by tracking their output measurements. Topology inference from indirect and/or incomplete dataset ...

IEEE2018

Bootstrap Percolation in Power-Law Random Graphs

Léo Hamed Amini

A bootstrap percolation process on a graph is an "infection" process which evolves in rounds. Initially, there is a subset of infected nodes and in each subsequent round each uninfected node which has at least infected neighbours becomes infected and remai ...

Springer2014

Metastable densities for the contact process on power law random graphs

Thomas Mountford, Daniel Rodrigues Valesin

We consider the contact process on a random graph with a fixed degree distribution given by a power law. We follow the work of Chatterjee and Durrett [2], who showed that for arbitrarily small infection parameter lambda, the survival time of the process is ...

Univ Washington, Dept Mathematics2013

Inferring Social Ties in Academic Networks Using Short-Range Wireless Communications

Jean-Pierre Hubaux, Murtuza Jadliwala, Kévin Clément Huguenin, Igor Bilogrevic

WiFi base stations are increasingly deployed in both public spaces and private companies, and the increase in their density poses a significant threat to the privacy of connected users. Prior studies have provided evidence that it is possible to infer the ...

2013

Community detection enhancement in networks using proper weighting and partial synchronization

Martin Hasler, Ali Ajdari Rad, Alireza Khadivi

A community in a network is a subset of vertices densely connected to each other, but less connected to the vertices outside. Many different approaches have been developed to find such structures in a given network, but the main drawback of most of the ava ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Spanning traceroutes over modular networks and general scaling degree distributions

Andrea Rinaldo, Amos Maritan

We analyze the class of networks characterized by modular structure where a sequence of l Erdos-Renyi random networks of size N >> 1 with random average degrees is joined by links whose structure must remain immaterial. We find that traceroutes spanning th ...

American Physical Society2010

High-Order Modeling of Hydraulic Power Plant in Islanded Power Network

François Avellan, Jean-Jacques Simond, Christophe Nicolet, Basile Kawkabani, Philippe Allenbach

Numerical simulations of the transient operation of an islanded power network subject to load rejections are performed. The islanded power network case study consists of a 1-GW hydroelectric power plant featuring four generating units, a long penstock, and ...

IEEE Institute of Electrical and Electronics Engineers2007

Interfaces and the edge percolation map of random directed networks

Paolo De Los Rios

The traditional node percolation map of directed networks is reanalyzed in terms of edges. In the percolated phase, edges can mainly organize into five distinct giant connected components, interfaces bridging the communication of nodes in the strongly conn ...

2007

Complex network analysis of free-energy landscapes

Paolo De Los Rios, David Gfeller

The kinetics of biomolecular isomerization processes, such as protein folding, is governed by a free-energy surface of high dimensionality and complexity. As an alternative to projections into one or two dimensions, the free-energy surface can be mapped in ...

National Academy of Sciences2007