Abstract
Motivation: Nowadays, reconstructed networks originated from various contexts become more complex and larger, which make them more difficult to figure out. Recognizing influential nodes helps us to comprehend these huge networks in a convenient way. To identify these nodes, several centrality measures based on the network properties are proposed. However, excessive variation of centrality measures complicates the process of choosing appropriate centrality measure for a given network. Therefore, a simple pipeline for comparing these measures and distinguishing which one rightfully points at the central nodes is required. Results: The CINNA R package conveniently has brought together all required methods for net-work centrality analysis. It contains network component segregation, calculation and prioritizing centralities, along with clustering and visualization functions. Availability: CINNA package is freely available from the R project at http://cran.r-project.org/, http://jafarilab-pasteur.com/content/software/CINNA.html.
