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
- Linking Ammonium Nitrate – Aluminum (AN-AL) Post-Blast Residues to PreBlast Explosive Materials Using Isotope Ratio and Trace Elemental Analysis for Source Attribution
- Examining the Relationship between Aptamer Complexity and Molecular Discrimination of a Low-Epitope Target
- Extraction of Ignitable Liquid Residues by Dynamic Capillary Headspace Sampling and Comparison to the Carbon Strip Method