ArticleScientific reports2023
Estimation and update of betweenness centrality with progressive algorithm and shortest paths approximation.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrative bioinformatics frameworks for abdominal aortic aneurysm using GWAS meta-analysis, biological network construction, and structural modeling.Scientific reports · 2025Pooled it
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- Network-constrained Random Lasso for biologically interpretable gene network inference across unequal sample sizes.PloS one · 2026Article
- Why are toilets not used? Using system effects modelling to understand stakeholder perceptions on the impacts and barriers to Taenia solium control in Eastern and Western Uganda.BMC veterinary research · 2025Article
- Mapping the current research landscape of metformin in cancer based on bibliometric analysis.Discover oncology · 2025Article
- Network Segregation and Integration Changes in Healthy Aging: Evidence From EEG Subbands During the Visual Short-Term Memory Binding Task.The European journal of neuroscience · 2025Article
- The association of social networks with the job performance of primary health care professionals: the mediating effect of knowledge sharing.Frontiers in medicine · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Betweenness centrality is one of the key measures of the node importance in a network. However, it is computationally intractable to calculate the exact betweenness centrality of nodes in large-scale networks. To solve this problem, we present an efficient CBCA (Centroids based Betweenness Centrality Approximation) algorithm based on progressive sampling and shortest paths approximation. Our algorithm firstly approximates the shortest paths by generating the network centroids according to the adjacency information entropy of the nodes; then constructs an efficient error estimator using the Monte Carlo Empirical Rademacher averages to determine the sample size which can achieve a balance with accuracy; finally, we present a novel centroid updating strategy based on network density and clustering coefficient, which can effectively reduce the computation burden of updating shortest paths in dynamic networks. The experimental results show that our CBCA algorithm can efficiently output high-quality approximations of the betweenness centrality of a node in large-scale complex networks.
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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.