In this paper, we analyze the robustness of centrality measure, which is widely used in network analyses, against missing nodes, missing links, and false links.
Through extensive simulations, we show the validity of our analysis. Moreover, by using our analytical models, we examine the robustness of centrality measures ...
Abstract. Research on network analysis, which is used to analyze large-scale and complex networks such as social networks, protein networks, and brain func-.
Through extensive simulations, we show the validity of our analysis, and suggest that our model can be used to analyze the robustness of not only degree ...
Analysis of the Robustness of Degree Centrality against Random Errors in Graphs. https://doi.org/10.1007/978-3-319-16112-9_3 · Full text. Journal: Studies in ...
In this paper, we analyze the robustness of centrality measure, which is widely used in network analyses, against missing nodes, missing links, and false links.
The results show that the accuracy of centrality measures declines smoothly and predictably with the amount of error.
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Analysis of the Robustness of Degree Centrality against Random Errors in Graphs · Sho TsugawaH. Ohsaki. Computer Science, Mathematics. CompleNet. 2015. TLDR.
Sho Tsugawa, Hiroyuki Ohsaki: Analysis of the Robustness of Degree Centrality against Random Errors in Graphs. CompleNet 2015: 25-36.
In the case of ER graphs the behavior is homogeneous: the true robustness depends primarily on the error intensity. The estimation errors are consistently low.