Alpha current flow betweenness centrality

TL;DR

This paper introduces two regularized versions of current flow betweenness centrality, -current flow and truncated -current flow, which are computationally efficient and maintain good correlation with the original measure.

Contribution

It proposes novel regularizations of current flow betweenness centrality that are faster to compute and preserve correlation with the original measure.

Findings

Regularized measures are computationally efficient.

Proposed measures correlate well with original current flow betweenness.

Methods enable analysis of large networks.

Abstract

A class of centrality measures called betweenness centralities reflects degree of participation of edges or nodes in communication between different parts of the network. The original shortest-path betweenness centrality is based on counting shortest paths which go through a node or an edge. One of shortcomings of the shortest-path betweenness centrality is that it ignores the paths that might be one or two steps longer than the shortest paths, while the edges on such paths can be important for communication processes in the network. To rectify this shortcoming a current flow betweenness centrality has been proposed. Similarly to the shortest path betwe has prohibitive complexity for large size networks. In the present work we propose two regularizations of the current flow betweenness centrality, \alpha-current flow betweenness and truncated \alpha-current flow betweenness, which can be computed fast and correlate well with the original current flow betweenness.