Abstract
| Original language | English |
|---|---|
| Article number | eaau9403 |
| Pages (from-to) | eaau9403 |
| Journal | Science Advances |
| Volume | 4 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 12 Dec 2018 |
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Keywords
- non normal network
- complex network
- non normal dynamic
- networked systems
- network models
- empirical networks
- Lotka-Volterra model
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Structure and dynamical behavior of non-normal networks. / Asllani, Malbor; Lambiotte, Renaud; Carletti, Timoteo.
In: Science Advances, Vol. 4, No. 12, eaau9403, 12.12.2018, p. eaau9403.Research output: Contribution to journal › Article
TY - JOUR
T1 - Structure and dynamical behavior of non-normal networks
AU - Asllani, Malbor
AU - Lambiotte, Renaud
AU - Carletti, Timoteo
PY - 2018/12/12
Y1 - 2018/12/12
N2 - We analyze a collection of empirical networks in a wide spectrum of disciplines and show that strong non-normality is ubiquitous in network science. Dynamical processes evolving on non-normal networks exhibit a peculiar behavior, as initial small disturbances may undergo a transient phase and be strongly amplified in linearly stable systems. In addition, eigenvalues may become extremely sensible to noise and have a diminished physical meaning. We identify structural properties of networks that are associated with non-normality and propose simple models to generate networks with a tunable level of non-normality. We also show the potential use of a variety of metrics capturing different aspects of non-normality and propose their potential use in the context of the stability of complex ecosystems.
AB - We analyze a collection of empirical networks in a wide spectrum of disciplines and show that strong non-normality is ubiquitous in network science. Dynamical processes evolving on non-normal networks exhibit a peculiar behavior, as initial small disturbances may undergo a transient phase and be strongly amplified in linearly stable systems. In addition, eigenvalues may become extremely sensible to noise and have a diminished physical meaning. We identify structural properties of networks that are associated with non-normality and propose simple models to generate networks with a tunable level of non-normality. We also show the potential use of a variety of metrics capturing different aspects of non-normality and propose their potential use in the context of the stability of complex ecosystems.
KW - non normal network
KW - complex network
KW - non normal dynamic
KW - networked systems
KW - network models
KW - empirical networks
KW - Lotka-Volterra model
UR - http://www.scopus.com/inward/record.url?scp=85058746347&partnerID=8YFLogxK
U2 - https://doi.org/10.1126/sciadv.aau9403
DO - https://doi.org/10.1126/sciadv.aau9403
M3 - Article
VL - 4
SP - eaau9403
JO - Science Advances
JF - Science Advances
SN - 2375-2548
IS - 12
M1 - eaau9403
ER -