ArticleScientific reports2018
A generalised significance test for individual communities in networks.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Multiomic network analysis reveals conserved and subtype-specific cooperative microRNA regulators in breast cancer.bioRxiv : the preprint server for biology · 2026Article
- Cross-phenotype genome-wide association study supports shared genetic etiology between skin and gastrointestinal tract diseases.Journal of biomedical research · 2026Article
- Article
- Gene communities in co-expression networks across different tissues.PLoS computational biology · 2023Article
- Article
- Genomics and phenomics of body mass index reveals a complex disease network.Nature communications · 2022Article
- Visualizing novel connections and genetic similarities across diseases using a network-medicine based approach.Scientific reports · 2022Article
- Article
- Unravelling the Shared Genetic Mechanisms Underlying 18 Autoimmune Diseases Using a Systems Approach.Frontiers in immunology · 2021Article
- Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules.Frontiers in genetics · 2020Article
- Computing the statistical significance of optimized communities in networks.Scientific reports · 2019Article
- Mapping the perturbome network of cellular perturbations.Nature communications · 2019Article
- Network Analysis Reveals the Latent Structure of Negative Symptoms in Schizophrenia.Schizophrenia bulletin · 2019Article
- Multiscale core-periphery structure in a global liner shipping network.Scientific reports · 2019Article
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
Many empirical networks have community structure, in which nodes are densely interconnected within each community (i.e., a group of nodes) and sparsely across different communities. Like other local and meso-scale structure of networks, communities are generally heterogeneous in various aspects such as the size, density of edges, connectivity to other communities and significance. In the present study, we propose a method to statistically test the significance of individual communities in a given network. Compared to the previous methods, the present algorithm is unique in that it accepts different community-detection algorithms and the corresponding quality function for single communities. The present method requires that a quality of each community can be quantified and that community detection is performed as optimisation of such a quality function summed over the communities. Various community detection algorithms including modularity maximisation and graph partitioning meet this criterion. Our method estimates a distribution of the quality function for randomised networks to calculate a likelihood of each community in the given network. We illustrate our algorithm by synthetic and 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.