Dependency Centrality from Bipartite Social Networks

TLDR: Gerdes et al. as mentioned in this paper introduced dependency centrality, a node-level measure of structural leadership in bipartite networks, which builds on Zhou et al.'s (2007) flow-based method to transform bipartitite data and captures additional information from the second mode that existing measures of centrality typically exclude.

Abstract: This paper introduces dependency centrality, a node-level measure of structural leadership in bipartite networks. The measure builds on Zhou et al.’s (2007) flow-based method to transform bipartite data and captures additional information from the second mode that existing measures of centrality typically exclude. Three previously published bipartite networks serve as test cases to demonstrate the extent of correlation among node-level centrality rankings derived from dependency centrality and those derived from canonical centrality measures: degree, closeness, betweenness, and eigenvector. Ultimately, dependency centrality appears to offer a novel means to measure importance in bipartite networks depicting social interactions. Author Luke M. Gerdes is an Assistant Professor in the Department of Behavioral Sciences & Leadership at the United States Military Academy in West Point, New York. Notes This work was supported by the Office of the Secretary of Defense, Minerva Initiative. The views expressed herein are those of the author and do not purport to represent the official policy or position of the United States Military Academy, the Department of the Army, the Department of Defense, or the United States Government. Please send all correspondence to Luke M. Gerdes, Department of Behavioral Sciences & Leadership, United States Military Academy. Email: luke.gerdes@usma.edu