Abstract
Research on network analysis, which is used to analyze large-scale and complex networks such as social networks, protein networks, and brain function networks, has been actively pursued. Typically, the networks used for network analyses will contain multiple errors because it is not easy to accurately and completely identify the nodes to be analyzed and the appropriate relationships among them. 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. We focus on the stability of node rankings based on degree centrality, and derive Top m and Overlap m , which evaluate the robustness of node rankings. 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 centrality but also other types of centrality measures. Moreover, by using our analytical models, we examine the robustness of degree centrality against random errors in graphs.
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Tsugawa, S., Ohsaki, H. (2015). Analysis of the Robustness of Degree Centrality against Random Errors in Graphs. In: Mangioni, G., Simini, F., Uzzo, S., Wang, D. (eds) Complex Networks VI. Studies in Computational Intelligence, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-319-16112-9_3
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DOI: https://doi.org/10.1007/978-3-319-16112-9_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16111-2
Online ISBN: 978-3-319-16112-9
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