Abstract:
: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.
journal_name
Sci Advjournal_title
Science advancesauthors
Asllani M,Lambiotte R,Carletti Tdoi
10.1126/sciadv.aau9403subject
Has Abstractpub_date
2018-12-12 00:00:00pages
eaau9403issue
12issn
2375-2548pii
aau9403journal_volume
4pub_type
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