Multiscale dynamical embeddings of complex networks

Michael T. Schaub, Jean Charles Delvenne, Renaud Lambiotte, Mauricio Barahona

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
Etat de la publicationPublié - 20 juin 2019

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