DocumentCode :
1910509
Title :
A Multiobjective and Evolutionary Clustering Method for Dynamic Networks
Author :
Folino, Francesco ; Pizzuti, Clara
Author_Institution :
Inst. for High Performance Comput. & Networking (ICAR), Italian Nat. Res. Council, Rende, Italy
fYear :
2010
fDate :
9-11 Aug. 2010
Firstpage :
256
Lastpage :
263
Abstract :
The discovery of evolving communities in dynamic networks is an important research topic that poses challenging tasks. Previous evolutionary based clustering methods try to maximize cluster accuracy, with respect to incoming data of the current time step, and minimize clustering drift from one time step to the successive one. In order to optimize both these two competing objectives, an input parameter that controls the preference degree of a user towards either the snapshot quality or the temporal quality is needed. In this paper the detection of communities with temporal smoothness is formulated as a multiobjective problem and a method based on genetic algorithms is proposed. The main advantage of the algorithm is that it automatically provides a solution representing the best trade-off between the accuracy of the clustering obtained, and the deviation from one time step to the successive. Experiments on synthetic data sets show the very good performance of the method compared to state-of-the-art approaches.
Keywords :
genetic algorithms; pattern clustering; dynamic networks; evolutionary clustering method; genetic algorithms; snapshot quality; temporal quality; Cathode ray tubes; Clustering algorithms; Communities; Cost function; Current measurement; Mutual information;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in Social Networks Analysis and Mining (ASONAM), 2010 International Conference on
Conference_Location :
Odense
Print_ISBN :
978-1-4244-7787-6
Electronic_ISBN :
978-0-7695-4138-9
Type :
conf
DOI :
10.1109/ASONAM.2010.23
Filename :
5562763
Link To Document :
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