ArticleiScience2021
Dimensionality reduction of complex dynamical systems.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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
7 citing papers in PubMed.
- Observing network dynamics through sentinel nodes.Nature communications · 2025Article
- Data-driven universal insights into tumorigenesis via hallmark networks.NPJ systems biology and applications · 2025Article
- Unraveling the mesoscale organization induced by network-driven processes.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Biodiversity and Constrained Information Dynamics in Ecosystems: A Framework for Living Systems.Entropy (Basel, Switzerland) · 2023Article
- Systematic analysis and optimization of early warning signals for critical transitions using distribution data.iScience · 2023Article
- Article
- Rising temperature drives tipping points in mutualistic networks.Royal Society open science · 2023Article
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
One of the outstanding problems in complexity science and engineering is the study of high-dimensional networked systems and of their susceptibility to transitions to undesired states as a result of changes in external drivers or in the structural properties. Because of the incredibly large number of parameters controlling the state of such complex systems and the heterogeneity of its components, the study of their dynamics is extremely difficult. Here we propose an analytical framework for collapsing complex N-dimensional networked systems into an S+1-dimensional manifold as a function of S effective control parameters with S << N. We test our approach on a variety of real-world complex problems showing how this new framework can approximate the system's response to changes and correctly identify the regions in the parameter space corresponding to the system's transitions. Our work offers an analytical method to evaluate optimal strategies in the design or management of networked systems.
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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.