ArticleNature communications2021
Percolation on feature-enriched interconnected systems.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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.
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Who cites it
8 citing papers in PubMed.
- Temporal self-similarity reveals percolation universality classes in complex networks.Nature communications · 2026Article
- Beyond networks: Toward adaptive models of biological complexity.Physics of life reviews · 2026Review
- Network dismantling by physical damage.Communications physics · 2025Article
- Robustness Analysis of Multilayer Infrastructure Networks Based on Incomplete Information Stackelberg Game: Considering Cascading Failures.Entropy (Basel, Switzerland) · 2024Article
- Interconnectedness enhances network resilience of multimodal public transportation systems for Safe-to-Fail urban mobility.Nature communications · 2023Article
- Feature-rich multiplex lexical networks reveal mental strategies of early language learning.Scientific reports · 2023Article
- Evaluating the Structural Robustness of Large-Scale Emerging Industry with Blurring Boundaries.Entropy (Basel, Switzerland) · 2022Article
- From the origin of life to pandemics: emergent phenomena in complex systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Percolation is an emblematic model to assess the robustness of interconnected systems when some of their components are corrupted. It is usually investigated in simple scenarios, such as the removal of the system's units in random order, or sequentially ordered by specific topological descriptors. However, in the vast majority of empirical applications, it is required to dismantle the network following more sophisticated protocols, for instance, by combining topological properties and non-topological node metadata. We propose a novel mathematical framework to fill this gap: networks are enriched with features and their nodes are removed according to the importance in the feature space. We consider features of different nature, from ones related to the network construction to ones related to dynamical processes such as epidemic spreading. Our framework not only provides a natural generalization of percolation but, more importantly, offers an accurate way to test the robustness of networks in realistic scenarios.
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.