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)
MATria: a unified centrality algorithm
NCJ Number
254202
Journal
Bmc Bioinformatics Volume: 20 Dated: 2019
Date Published
2019
Length
12 pages
Annotation
This article generalizes the results of any k centrality algorithms through the authors' iterative algorithm MATRIA, producing a single ranked and unified set of central nodes.
Abstract