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• W2501846427 abstract "The amounts of data are growing continuously. The data can make a benefit for the organization if they have the right plan to collect and analyze the data. In this paper, we examine data on research citation information. Many authors create interesting articles for propagating information to other researchers. The relationship network of researchers is also growing continuously. Learning network of researchers is necessary to find who has the most influence on others in the network. The researchers do not only have many papers but also they have co-authors who may stay in different communities. Adaptive information from various topics will make original papers. The combination of knowledge among researchers from various communities is a good way to create interesting papers. The aim of this article is to present a new measurement for author evaluation by using clustering coefficient and weighted degree centrality. The result will be used to rank researchers in order and analyze properties of the top 5 researchers. The ranking result can be comparable to the popular usage method, h-index. Hence, the new measurement for author evaluation using social network analysis measurement is a good way for author ranking." @default.
• W2501846427 created "2016-08-23" @default.
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• W2501846427 date "2016-05-01" @default.
• W2501846427 modified "2023-09-26" @default.
• W2501846427 title "Identifying important nodes in scientific publications using co-authorship network" @default.
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• W2501846427 doi "https://doi.org/10.1109/ict-ispc.2016.7519224" @default.
• W2501846427 hasPublicationYear "2016" @default.
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