Centrality Measures

In this study, the structure of co-authorship networks was analyzed using the Macro-level and Micro-level Approach. The study also examined the relationship between centrality measures and citation performance. The research reported here favors a bibliometric approach aiming to visualize the co-authorship networks building on the network analysis method. The population of the research consisted of 24308 authors of the indexed papers in the WOS database. To collect the data pertaining to the citation performance of a given researcher, the articles the given researchers published in a period of three years were taken into account. The citation data (excluding the self-citation) of a given article were gathered considering a three-year lapse after its publication. The results suggested that the mean of co-authorship for each article was 2.8 authors and the ratio of co-authored articles to single-authored was 89.4%. Also, this said co-authorship network was a network of "small world" and lacked "scale". The achieved findings illustrated that any increase in the rate of centrality and betweenness centrality of articles led to an increase in the amount of received citations. That said, no significant relationship was found between closeness centrality and the amount of received citations in this area. In this period, the variable of closeness centrality was able to predict the significance for the citation performance of the researchers

In this paper, we present a novel ranking technique that we developed in the context of an application that arose in a Service Delivery setting. We consider the problem of ranking agents of a service organization. The service agents typically need to interact with other service agents to accomplish the end goal of resolving customer requests. Their ranking needs to take into account two aspects: firstly, their importance in the network structure that arises as a result of their interactions, and secondly, the value generated by the interactions involving them. We highlight several other applications which have the common theme of ranking the participants of a value creation process based on the network structure of their interactions and the value generated by their interactions. We formally present the problem and describe the modeling technique which enables us to encode the value of interaction in the graph. Our ranking algorithm is based on extension of eigen value methods. We p...

In this paper, we have developed a network of 20 amino acids based on a distance matrix of amino acids. This distance matrix is obtained by considering the transition and transversion mutation of codons. We have proposed that the evolutionary pattern of amino acids is reflected throughout this network. We have discussed different measures of centrality: degree centrality, closeness centrality, betweenness centrality and eigenvector centrality, concerning this network and investigated the comparative impact of the amino acids. We have also explored the correlation coefficients between the different centrality measures checking the assortativity of the network. Further, we have explored three network parameters: namely clustering coefficient, degree of distribution and skewness.