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Markovian Random Walk Betweenness for Computational Biological Networks

Rachel Sousa and Michael Kupperman. Oregon State, University Department of Mathematics

Final project for CS 446, Winter 2020. Oregon State University. Project released under the GNU GPLv3 license.

Abstract

The betweenness centrality of a biological network is important in determining the relative necessity of nodes within the graph topology by considering paths traversing the graph. Here, we examine and compare different notions of betweenness centrality of a transcription–factor–to–target-gene interaction network in the model species, E. coli. This project contrasts two approaches to the betweenness centrality: one considering the geodesic paths between nodes that the node of interest lies on and a biased random walks where the flow of information is not optimal.

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