Multiscale dynamical embeddings of complex networks

Résultats de recherche: Contribution à un journal/une revueArticle

Résumé

Complex systems and relational data are often abstracted as dynamical processes on networks. To understand, predict, and control their behavior, a crucial step is to extract reduced descriptions of such networks. Inspired by notions from control theory, we propose a time-dependent dynamical similarity measure between nodes, which quantifies the effect a node-input has on the network. This dynamical similarity induces an embedding that can be employed for several analysis tasks. Here we focus on (i) dimensionality reduction, i.e., projecting nodes onto a low-dimensional space that captures dynamic similarity at different timescales, and (ii) how to exploit our embeddings to uncover functional modules. We exemplify our ideas through case studies focusing on directed networks without strong connectivity and signed networks. We further highlight how certain ideas from community detection can be generalized and linked to control theory, by using the here developed dynamical perspective.

langue originaleAnglais
Numéro d'article062308
Nombre de pages18
journalPhysical Review E
Volume99
Numéro de publication6
Les DOIs
étatPublié - 20 juin 2019

Empreinte digitale

Complex Networks
embedding
Control Theory
control theory
Vertex of a graph
Strong Connectivity
Community Detection
Directed Network
Dimensionality Reduction
Signed
Similarity Measure
Complex Systems
Time Scales
Quantify
complex systems
Predict
Module
modules
Similarity

Citer ceci

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Multiscale dynamical embeddings of complex networks. / Schaub, Michael T.; Delvenne, Jean Charles; Lambiotte, Renaud; Barahona, Mauricio.

Dans: Physical Review E, Vol 99, Numéro 6, 062308, 20.06.2019.

Résultats de recherche: Contribution à un journal/une revueArticle

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AU - Schaub, Michael T.

AU - Delvenne, Jean Charles

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AU - Barahona, Mauricio

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