ArticleBMC bioinformatics2014
Exploring community structure in biological networks with random graphs.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Neuromorphic hierarchical modular reservoirs.Nature communications · 2026Article
- Multiomic network analysis reveals conserved and subtype-specific cooperative microRNA regulators in breast cancer.bioRxiv : the preprint server for biology · 2026Article
- Integrative network analysis reveals organizational principles of the endocannabinoid system.Journal of cannabis research · 2026Article
- Network construction using sparse Gaussian graphical model based on GWAS summary statistics.Scientific reports · 2025Article
- Article
- Integrating graph and reinforcement learning for vaccination strategies in complex networks.Scientific reports · 2024Article
- Graph 'texture' features as novel metrics that can summarize complex biological graphs.Physics in medicine and biology · 2023Article
- GRANDPA: GeneRAtive network sampling using degree and property augmentation applied to the analysis of partially confidential healthcare networks.Applied network science · 2023Article
- Review
- Modularity in Biological Networks.Frontiers in genetics · 2021Review
- Community Detection in Large-Scale Bipartite Biological Networks.Frontiers in genetics · 2021Review
- Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules.Frontiers in genetics · 2020Article
- Topological and functional comparison of community detection algorithms in biological networks.BMC bioinformatics · 2019Article
- Genetic interaction networks mediate individual statin drug response inNPJ systems biology and applications · 2019Article
- Article
- A Comparative Study of Cluster Detection Algorithms in Protein-Protein Interaction for Drug Target Discovery and Drug Repurposing.Frontiers in pharmacology · 2019Article
- Functional diversity of topological modules in human protein-protein interaction networks.Scientific reports · 2017Article
- Unraveling the disease consequences and mechanisms of modular structure in animal social networks.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- A seed-expanding method based on random walks for community detection in networks with ambiguous community structures.Scientific reports · 2017Article
- DISCOVERY OF FUNCTIONAL AND DISEASE PATHWAYS BY COMMUNITY DETECTION IN PROTEIN-PROTEIN INTERACTION NETWORKS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCommunity structure is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system's functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to identify the community structure in an empirical network can be difficult, however, as the many algorithms available are based on a variety of cost functions and are difficult to validate. Even when community structure is identified in an empirical system, disentangling the effect of community structure from other network properties such as clustering coefficient and assortativity can be a challenge.
resultsHere, we develop a generative model to produce undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible. Additionally, we demonstrate two important applications of our model: (a) to generate networks that can be used to benchmark existing and new algorithms for detecting communities in biological networks; and (b) to generate null models to serve as random controls when investigating the impact of complex network features beyond the byproduct of degree and modularity in empirical biological networks.
conclusionOur model allows for the systematic study of the presence of community structure and its impact on network function and dynamics. This process is a crucial step in unraveling the functional consequences of the structural properties of biological systems and uncovering the mechanisms that drive these systems.
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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.