Computing 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. Through tests on three biological networks, the authors of this article demonstrate evident and balanced correlations with the results of these k algorithms. They also improve its speed through GPU parallelism. The 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. (publisher abstract modified)
Downloads
Similar Publications
- Cardiac Genetic Test Yields and Genotype-phenotype Correlations from Large Cohort Investigated by Medical Examiner's Office
- Estimation of Sex Assigned at Birth Using Dental Crown and Cervical Measurements in a Modern Global Sample
- Raman Spectroscopy for the Time since Deposition Estimation of a Menstrual Bloodstain