Identifying the Evolving Communities in Dynamic Networks
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 5
Abstract
The discovery of evolving communities in dynamic multimodal networks with heterogeneous actors is an important research topic possessing challenging tasks. Evolutionary clustering is a recent framework for clustering dynamic multimodal networks introducing temporal smoothness concept inside the community structure detection. Evolutionary based clustering approaches try to maximize cluster accuracy with respect to incoming data of the current time step, and minimize cluster drift from one time step to the successive one. A temporally regularized framework and its convergence property are used 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 multi objective 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.
Authors and Affiliations
P. Divya, Dr. P. Rajkumar
Keywords
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