Towards a Methodology for Validation of Centrality Measures in Complex Networks

Abstract

BackgroundLiving systems are associated with Social networks — networks made up of nodes, some of which may be more important in various aspects as compared to others. While different quantitative measures labeled as “centralities” have previously been used in the network analysis community to find out influential nodes in a network, it is debatable how valid the centrality measures actually are. In other words, the research question that remains unanswered is: how exactly do these measures perform in the real world? So, as an example, if a centrality of a particular node identifies it to be important, is the node actually important?PurposeThe goal of this paper is not just to perform a traditional social network analysis but rather to evaluate different centrality measures by conducting an empirical study analyzing exactly how do network centralities correlate with data from published multidisciplinary network data sets.MethodWe take standard published network data sets while using a random network to establish a baseline. These data sets included the Zachary's Karate Club network, dolphin social network and a neural network of nematode Caenorhabditis elegans. Each of the data sets was analyzed in terms of different centrality measures and compared with existing knowledge from associated published articles to review the role of each centrality measure in the determination of influential nodes.ResultsOur empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes.

Opportunities and methodological challenges in EEG and MEG resting state functional brain network research

“…Each local network metric captures specific information about the network topology, making it appropriate for certain analysis, depending on the specific research question. For example, the eigenvector centrality gives a more accurate estimation of the centrality of a specific node in the network than degree (Batool et al , 2014), and has lower computational costs than betweenness centrality (Lohmann et al , 2010), it is less sensitive for the detection of hubs in modules (Joyce et al , 2010) and has specific normalization problems (Ruhnau, 2000).…”

Social Network Analysis of Scientific Articles Published by Food Policy

“…These links create the structure of the network. While not without their accompanying problems, as noted by [22], structural and topological attributes have been used in several studies to understand the nuances and the importance of human behavior in social networks [23].…”

Approximating web communities using subspace decomposition

“…It identifies and measures participation of edges and vertices in a community structure [2]. Degree centrality is also used to detect intermediate edges between communities [14].…”