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|Title:||An evolutionary approach for detecting communities in social networks||Authors:||Öztürk, K.
|Issue Date:||31-Jul-2017||Publisher:||Association for Computing Machinery, Inc.||Source:||Ozturk, K., Polat, F., & Ozyer, T. (2017, July). An Evolutionary Approach for Detecting Communities in Social Networks. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (pp. 966-973). ACM.||Abstract:||Rapid development and wide usage of social networking applications have enabled large amounts of valuable data which can be analyzed for various reasons by companies, governments, non-profit organizations such as UN. This paper presents an evolutionary approach for detecting communities in social networks. We formulated a genetic algorithm that does not require the number of communities as input and is able to detect communities effectively in a very fast way. The performance of the proposed method is compared to its counterparts in order to show that good results can be generated. Additionally, we have done experiments using Newman’s Spectral Clustering Method as a pre-processing step and it gave much better results. © 2017 Association for Computing Machinery.||Description:||9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2017 : Sydney; Australia)||URI:||https://dl.acm.org/citation.cfm?doid=3110025.3110157
|Appears in Collections:||Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering|
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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