ArticleNature communications2024
Reconstructing the evolution history of networked complex systems.
Article in Nature communications, 2024. 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
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
8 citing papers in PubMed.
- IdentifyingInfluential Nodes in Complex Networks Based on the Integration of Smallest-Cycle and Non-Smallest-Cycle Features.Entropy (Basel, Switzerland) · 2026Article
- Research progress on the Sonic Hedgehog signaling pathway in the central nervous system: Novel insights.Neural regeneration research · 2026Article
- How objects become psychologically available: in-itself information and informational mediation for theoretical psychology.Frontiers in psychology · 2026Review
- Evolution of competitive systems in nature.Scientific reports · 2025Article
- Protein evolution as a complex system.Nature chemical biology · 2025Article
- Network analysis with quantum dynamics clarifies why photosystem II exploits both chlorophyll a and b.Science advances · 2025Article
- Nano-Topography Enhanced Topological-Cell-Analysis in Radiation-Therapy.Advanced healthcare materials · 2025Review
- Reconstructing the evolution history of networked complex systems.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
The evolution processes of complex systems carry key information in the systems' functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks.
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.