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.
Highlights
Living systems are associated with Social networks — networks involve diffusion of information from one node to the other, some of which may be more important than others
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
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
Summary
Living systems are associated with Social networks — networks involve diffusion of information from one node to the other, some of which may be more important than others. Networks allow for modeling complex interactions of components in the form of a standard set of representations [25] These representations can be used to model a wide range of complex systems — systems as diverse and ranging from those involving the co-expression of genes to interaction of online peers in a peer-to-peer file sharing network or humans connecting together in a social community to animals communicating and interacting with each other [9]. In all such networks, a key dynamical process is the fact that each network spreads some quantity of information from one node to the other. It is pertinent to note here that networks have previously been described as an alternative approach to modeling these Complex Adaptive Systems (CAS) [26], in addition to agent-based [24]
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