ArticleScientific reports2014
Revealing the hidden language of complex networks.
Article in Scientific reports, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 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
74 citing papers in PubMed.
- A New Framework to Empower Ecosystem Assessment Through the Integration of eDNA Inventories, Graph Theory and Niche Modelling.Ecology letters · 2026Article
- Is the molecular microenvironment of the latent HIV reservoir predictable using deep learning approaches?Journal of virology · 2026Review
- Combining graphlets and random walks for capturing complex network topology.Scientific reports · 2026Article
- First insights into microbial changes within an Inflammatory Bowel Disease Family Cohort study.Gut microbes · 2025Article
- From network biology to immunity: potential longitudinal biomarkers for targeting the network topology of the HIV reservoir.Journal of translational medicine · 2025Review
- An egonet based approach to effective weighted network comparison.Scientific reports · 2025Article
- [1]The human gut microbiota in IBD, characterizing hubs, the core microbiota and terminal nodes: a network-based approach.BMC microbiology · 2025Article
- Generating random graphs with prescribed graphlet frequency bounds derived from probabilistic networks.PloS one · 2025Article
- Effect of implant abutment surface treatments on bacterial biofilm composition and structure.Journal of oral microbiology · 2025Article
- Publication-driven consistency in food web structures: Implications for comparative ecology.Ecology · 2025Article
- Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks.Scientific reports · 2024Article
- Graphlet-based hyperbolic embeddings capture evolutionary dynamics in genetic networks.Bioinformatics (Oxford, England) · 2024Article
- Assessing peri-implant bacterial community structure: the effect of microbiome sample collection method.BMC oral health · 2024Article
- Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction.Briefings in bioinformatics · 2024Article
- Disentangling microbial networks across pelagic zones in the tropical and subtropical global ocean.Nature communications · 2024Article
- Ascertaining injury risk issues through big data analysis: text-mining based analysis of national emergency response data.Frontiers in public health · 2024Article
- Improving Robustness of High-Low-Order Coupled Networks against Malicious Attacks Based on a Simulated Annealing Algorithm.Entropy (Basel, Switzerland) · 2023Article
- A multi-omics integrative approach unravels novel genes and pathways associated with senescence escape after targeted therapy in NRAS mutant melanoma.Cancer gene therapy · 2023Article
- Metrics for network comparison using egonet feature distributions.Scientific reports · 2023Article
- Shortcomings of reusing species interaction networks created by different sets of researchers.PLoS biology · 2023Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in a network, can characterize a network's structure and also provide a clear real-world interpretation. Given this insight, we develop a framework for analysing and comparing networks, which outperforms all existing ones. We demonstrate its strength by uncovering novel relationships between seemingly unrelated networks, such as Facebook, metabolic, and protein structure networks. We also use it to track the dynamics of the world trade network, showing that a country's role of a broker between non-trading countries indicates economic prosperity, whereas peripheral roles are associated with poverty. This result, though intuitive, has escaped all existing frameworks. Finally, our approach translates network topology into everyday language, bringing network analysis closer to domain scientists.
Indexed as
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.