An Analysis of Centrality Measures for Complex and Social Networks

Abstract

Measures of complex network analysis, such as vertex centrality, have the potential to unveil existing network patterns and behaviors. They contribute to the understanding of networks and their components by analyzing their structural properties, which makes them useful in several computer science domains and applications. Unfortunately, there is a large number of distinct centrality measures and little is known about their common characteristics in practice. By means of an empirical analysis, we aim at a clear understanding of the main centrality measures available, unveiling their similarities and differences in a large number of distinct social networks. Our experiments show that the vertex centrality measures known as information, eigenvector, subgraph, walk betweenness and betweenness can distinguish vertices in all kinds of networks with a granularity performance at 95%, while other metrics achieved a considerably lower result. In addition, we demonstrate that several pairs of metrics evaluate the vertices in a very similar way, i.e. their correlation coefficient values are above 0.7. This was unexpected, considering that each metric presents a quite distinct theoretical and algorithmic foundation. Our work thus contributes towards the development of a methodology for principled network analysis and evaluation.

Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model

“…In a nutshell, centrality measures quantify the centrality of each vertex of a network, usually creating a rank of vertices, or the entire network itself, by computing how centralized a given network is [3] [5]. The term centrality in this context can admit several meanings depending on the application domain under analysis.…”

“…The vertex centrality measures are important to identify elements behavior and roles within a network and are usually employed to create ranks among vertices that are used as comparison factors with others domain specific metrics [3] [4]. Such metrics are characterized by deterministic algorithms.…”

“…Each algorithm and measure are distinct from the others although they frequently share a common goal and produce similar/close results. Some of these metrics are adaptations from the others to enable their use on digraphs or weighted networks [3]. In this work, we will use four centrality measures, namely: degree, eigenvector, betweenness and closeness.…”

Network science algorithms for mobile networks.

“…This is a very useful measure and it has important applications [31][32][33][34][35]. This can be used to discover central nodes that can has important effect on other nodes.…”

“…We focus here in this dissertation on five similarity measures that can be used effectively for mobile networks where the network is changing dynamically over time [31][32][33][34][35][36].…”