ArticleJournal of the Royal Society, Interface2013
The diminishing role of hubs in dynamical processes on complex networks.
Article in Journal of the Royal Society, Interface, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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.
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Who cites it
16 citing papers in PubMed.
- Network Structure of Post-Traumatic Stress and Social/Emotional/Behavioral Difficulties in a Post-Earthquake Child and Adolescent Sample.European journal of investigation in health, psychology and education · 2025Article
- Cascades Towards Noise-Induced Transitions on Networks Revealed Using Information Flows.Entropy (Basel, Switzerland) · 2024Article
- Module control of network analysis in psychopathology.iScience · 2024Article
- Transformation starts at the periphery of networks where pushback is less.Scientific reports · 2024Article
- eHealth tools use and mental health: a cross-sectional network analysis in a representative sample.Scientific reports · 2024Article
- A network approach can improve eating disorder conceptualization and treatment.Nature reviews psychology · 2022Article
- Evaluating the performance of multivariate indicators of resilience loss.Scientific reports · 2021Article
- Optimal Microbiome Networks: Macroecology and Criticality.Entropy (Basel, Switzerland) · 2019Article
- Locating Order-Disorder Phase Transition in a Cardiac System.Scientific reports · 2018Article
- Resisting Influence: How the Strength of Predispositions to Resist Control Can Change Strategies for Optimal Opinion Control in the Voter Model.Frontiers in robotics and AI · 2018Article
- A study on the dynamics of temporary HIV treatment to assess the controversial outcomes of clinical trials: An in-silico approach.PloS one · 2018Article
- Interactional dynamics of same-sex marriage legislation in the United States.Royal Society open science · 2017Article
- Dynamic versus static biomarkers in cancer immune checkpoint blockade: unravelling complexity.Nature reviews. Drug discovery · 2017Review
- Efficient collective influence maximization in cascading processes with first-order transitions.Scientific reports · 2017Article
- Reconstructing propagation networks with temporal similarity.Scientific reports · 2015Article
- The relative ineffectiveness of criminal network disruption.Scientific reports · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is notoriously difficult to predict the behaviour of a complex self-organizing system, where the interactions among dynamical units form a heterogeneous topology. Even if the dynamics of each microscopic unit is known, a real understanding of their contributions to the macroscopic system behaviour is still lacking. Here, we develop information-theoretical methods to distinguish the contribution of each individual unit to the collective out-of-equilibrium dynamics. We show that for a system of units connected by a network of interaction potentials with an arbitrary degree distribution, highly connected units have less impact on the system dynamics when compared with intermediately connected units. In an equilibrium setting, the hubs are often found to dictate the long-term behaviour. However, we find both analytically and experimentally that the instantaneous states of these units have a short-lasting effect on the state trajectory of the entire system. We present qualitative evidence of this phenomenon from empirical findings about a social network of product recommendations, a protein-protein interaction network and a neural network, suggesting that it might indeed be a widespread property in nature.
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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.