ArticleScience advances2018
Structure and dynamical behavior of non-normal networks.
Article in Science advances, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed.
- Synaptic-state-dependent plasticity routes learning signals in recurrent circuits.bioRxiv : the preprint server for biology · 2026Article
- Task-Parametrized dynamics: Representation of time and decisions in recurrent neural networks.Journal of computational neuroscience · 2026Article
- Extreme vulnerability to intruder attacks destabilizes network dynamics.Nature communications · 2026Article
- Transiently amplified fluctuations assemble dissipative materials.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The frequency response of networks as open systems.Nature communications · 2026Article
- Neural dynamics outside task-coding dimensions drive decision trajectories through transient amplification.bioRxiv : the preprint server for biology · 2025Article
- Quantum Mpemba Effect from Non-Normal Dynamics.Entropy (Basel, Switzerland) · 2025Article
- Mediodorsal thalamus regulates task uncertainty to enable cognitive flexibility.Nature communications · 2025Article
- Statistical Mechanics of Directed Networks.Entropy (Basel, Switzerland) · 2025Article
- Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics.Science advances · 2024Article
- The efficiency of synchronization dynamics and the role of network syncreactivity.Nature communications · 2024Article
- How biotic interactions structure species' responses to perturbations.Proceedings. Biological sciences · 2024Article
- Recall tempo of Hebbian sequences depends on the interplay of Hebbian kernel with tutor signal timing.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Two wrongs do not make a right: the assumption that an inhibitor acts as an inverse activator.Journal of mathematical biology · 2024Article
- Exploring Flip Flop memories and beyond: training Recurrent Neural Networks with key insights.Frontiers in systems neuroscience · 2024Article
- Reactivity of complex communities can be more important than stability.Nature communications · 2023Article
- Influence and influenceability: global directionality in directed complex networks.Royal Society open science · 2023Article
- Strong connectivity in real directed networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Different eigenvalue distributions encode the same temporal tasks in recurrent neural networks.Cognitive neurodynamics · 2023Article
- Excitatory-inhibitory recurrent dynamics produce robust visual grids and stable attractors.Cell reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.