ArticleBMC bioinformatics2019
MATria: a unified centrality algorithm.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Differences in gut microbiota between Dutch and South-Asian Surinamese: potential implications for type 2 diabetes mellitus.Scientific reports · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundComputing centrality is a foundational concept in social networking that involves finding the most "central" or important nodes. In some biological networks defining importance is difficult, which then creates challenges in finding an appropriate centrality algorithm.
resultsWe instead generalize the results of any k centrality algorithms through our iterative algorithm MATRIA, producing a single ranked and unified set of central nodes. Through tests on three biological networks, we demonstrate evident and balanced correlations with the results of these k algorithms. We also improve its speed through GPU parallelism.
conclusionsOur results show iteration to be a powerful technique that can eliminate spatial bias among central nodes, increasing the level of agreement between algorithms with various importance definitions. GPU parallelism improves speed and makes iteration a tractable problem for larger networks.
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