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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 285))

Abstract

Dynamic graphs are ubiquitous in real world applications. They can be found, e.g. in biology, neuroscience, computer science, medicine, social networks, the World Wide Web. There is a great necessity and interest in analyzing these dynamic graphs efficiently. Typically, analysis methods from classical data mining and network theory have been studied separately in different fields of research. Dealing with complex networks in real world applications, there is a need to perform interdisciplinary research by combining techniques of different fields. In this paper, we analyze dynamic graphs from two different applications, i.e. social science and neuroscience. We exploit the edge weights in both types of networks to answer distinct questions in the respective fields of science. First, for the representation of edge weights in a social network graph we propose a method to efficiently represent the strength of a relation between two entities based on events involving both entities. Second, we correlate graph measures of electroencephalographic activity networks with clinical variables to find good predictors for patients with visual field damages.

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Held, P., Moewes, C., Braune, C., Kruse, R., Sabel, B.A. (2013). Advanced Analysis of Dynamic Graphs in Social and Neural Networks. In: Borgelt, C., Gil, M., Sousa, J., Verleysen, M. (eds) Towards Advanced Data Analysis by Combining Soft Computing and Statistics. Studies in Fuzziness and Soft Computing, vol 285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30278-7_17

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  • DOI: https://doi.org/10.1007/978-3-642-30278-7_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30277-0

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