A Novel Measure of Edge Centrality in Social Networks

TL;DR

This paper introduces a new measure called k-path edge centrality for assessing the importance of edges in social networks, along with an efficient algorithm suitable for large-scale analysis.

Contribution

It generalizes the k-path centrality concept to edges and provides an efficient O(km) algorithm for large network analysis.

Findings

Algorithm runs in O(k m) time, scalable for large networks.

Performance tested on large online social network datasets.

Effective in identifying important edges in social networks.

Abstract

The problem of assigning centrality values to nodes and edges in graphs has been widely investigated during last years. Recently, a novel measure of node centrality has been proposed, called k-path centrality index, which is based on the propagation of messages inside a network along paths consisting of at most k edges. On the other hand, the importance of computing the centrality of edges has been put into evidence since 1970's by Anthonisse and, subsequently by Girvan and Newman. In this work we propose the generalization of the concept of k-path centrality by defining the k-path edge centrality, a measure of centrality introduced to compute the importance of edges. We provide an efficient algorithm, running in O(k m), being m the number of edges in the graph. Thus, our technique is feasible for large scale network analysis. Finally, the performance of our algorithm is analyzed, discussing the results obtained against large online social network datasets.