TY  - CHAP
ID  - held_advanced_2012
T1  - Advanced Analysis of Dynamic Graphs in Social and Neural Networks
A1  - Held, Pascal
A1  - Moewes, Christian
A1  - Braune, Christian
A1  - Kruse, Rudolf
A1  - Sabel, Bernhard A.
ED  - Borgelt, Christian
ED  - Gil, María Ángeles
ED  - Sousa, João M. C.
ED  - Verleysen, Michel
TI  - Towards Advanced Data Analysis by Combining Soft Computing and Statistics
T3  - Studies in Fuzziness and Soft Computing
Y1  - 2013
VL  - 285
SP  - 205
EP  - 222
PB  - Springer
AD  - Berlin Heidelberg
SN  - 978-3-642-30277-0
UR  - http://link.springer.com/chapter/10.1007/978-3-642-30278-7_17
M2  - doi: 10.1007/978-3-642-30278-7_17
KW  - dynamic graphs
KW  - EEG
KW  - Enron dataset
KW  - neuroimaging
KW  - neuroscience
KW  - social network analysis
N2  - 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.
ER  -