Networks are used to model a variety of highly interconnected systems, both in nature and man-made world of technology. Recently network models are now commonplace not only in the hard sciences but also in various technological, social and biological scenarios. Graphs and linear algebra have been used to model social interactions. Since its introduction by Euler in eighteen century, graph theory has proven its important applications in many different scientific fields. Similar results were obtained for real-world networks. Logarithmic function in particular has potential as a centrality measure. Networks generated by using random graph models. Various experiments are conducted for different Measures based on general matrix functions and in particular, the logarithmic,Ĭosine, sine, and hyperbolic functions. We extend this idea and examine the centrality We show how the Katz centrality can be turned into degree and eigenvectorĬentrality by considering limiting cases. We examine node centrality measures such as degree,Ĭloseness, eigenvector, Katz and subgraph centrality for undirected networks. The popular method for doing this is to calculateĪ measure of centrality. One of the huge network structureĬhallenges is the identification of the node(s) with an outstanding structural Network are the key issues in network analysis. Place in the network, determination of the structure of the network andĬommunity and description of the interactions between various elements of the Investigation of the dynamic phenomena taking Network is considered naturally as a wide range ofĭifferent contexts, such as biological systems, social relationships as well as
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