ArticleNature communications2015
Quantifying randomness in real networks.
Article in Nature communications, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed, 186 citations in OpenAlex.
- Network hypermotifs in biological systems: structure, dynamics, and functional implications.NPJ systems biology and applications · 2026Review
- A simulated annealing algorithm for randomizing weighted networks.Nature computational science · 2025Article
- Analyzing Mega-mobility Systems in Smart Cities: A Macro-Micro Integration with Feedback Paradigm Empowered by Artificial Intelligence.Research (Washington, D.C.) · 2025Review
- Structural robustness of networks with degree-degree correlations between second-nearest neighbors.PloS one · 2025Article
- Initialisation and network effects in decentralised federated learning.Applied network science · 2025Article
- Compression-based inference of network motif sets.PLoS computational biology · 2024Article
- Intrinsic dimension as a multi-scale summary statistics in network modeling.Scientific reports · 2024Article
- On the friendship paradox and inversity: A network property with applications to privacy-sensitive network interventions.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Directed Network Comparison Using Motifs.Entropy (Basel, Switzerland) · 2024Article
- Article
- Hyper-Null Models and Their Applications.Entropy (Basel, Switzerland) · 2023Article
- Asymptotically Optimal Adversarial Strategies for the Probability Estimation Framework.Entropy (Basel, Switzerland) · 2023Article
- Trust based attachment.PloS one · 2023Article
- Models of similarity in complex networks.PeerJ. Computer science · 2023Article
- Clarifying How Degree Entropies and Degree-Degree Correlations Relate to Network Robustness.Entropy (Basel, Switzerland) · 2022Article
- Emergence of dynamic properties in network hypermotifs.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Evolution of Cohesion between USA Financial Sector Companies before, during, and Post-Economic Crisis: Complex Networks Approach.Entropy (Basel, Switzerland) · 2022Article
- Article
- The impact of the suppression of highly connected protein interactions on the corona virus infection.Scientific reports · 2022Article
- Grand Canonical Ensembles of Sparse Networks and Bayesian Inference.Entropy (Basel, Switzerland) · 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
12 authors at 11 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks--the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain--and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.
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.