An Evolutionary Multiobjective Approach for Community Discovery in Dynamic Networks

The discovery of evolving communities in dynamic networks is an important research topic that poses challenging tasks. Evolutionary clustering is a recent framework for clustering dynamic networks that introduces the concept of temporal smoothness inside the community structure detection method. Evo...

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Vydáno v:IEEE transactions on knowledge and data engineering Ročník 26; číslo 8; s. 1838 - 1852
Hlavní autoři: Folino, Francesco, Pizzuti, Clara
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Communities
Current measurement
Data mining
Evolutionary computing and genetic algorithms
Genetic algorithms
Optimization
Sociology
Statistics
Time measurement
community discovery
Evolutionary clustering
complex networks
dynamic networks
ISSN:1041-4347, 1558-2191
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Shrnutí:The discovery of evolving communities in dynamic networks is an important research topic that poses challenging tasks. Evolutionary clustering is a recent framework for clustering dynamic networks that introduces the concept of temporal smoothness inside the community structure detection method. Evolutionary-based clustering approaches 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 when compared with state-of-the-art approaches.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2013.131