In this section
@article{Schaub:2019:10.1103/PhysRevE.99.062308,
author = {Schaub, MT and Delvenne, JC and Lambiotte, R and Barahona, M},
doi = {10.1103/PhysRevE.99.062308},
journal = {Physical Review E},
pages = {062308--1--062308--18},
title = {Multiscale dynamical embeddings of complex networks},
url = {http://dx.doi.org/10.1103/PhysRevE.99.062308},
volume = {99},
year = {2019}
}
TY - JOUR
AB - 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.
AU - Schaub,MT
AU - Delvenne,JC
AU - Lambiotte,R
AU - Barahona,M
DO - 10.1103/PhysRevE.99.062308
EP - 1
PY - 2019///
SN - 1539-3755
SP - 062308
TI - Multiscale dynamical embeddings of complex networks
T2 - Physical Review E
UR - http://dx.doi.org/10.1103/PhysRevE.99.062308
UR - https://journals.aps.org/pre/abstract/10.1103/PhysRevE.99.062308
UR - http://hdl.handle.net/10044/1/71570
VL - 99
ER -
Artificial Intelligence Network
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Imperial College London
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