Abstract
In a social network, individuals often simultaneously belong to multiple social communities; therefore, the detection of relationships among individuals is very important. However, most of community detection methods only apply a single relationship in dynamic social networks with multi-relationships among individuals. Therefore, this study proposes a CNET Hierarchical Division Algorithm (CHDA) to detect communities efficiently. Experimental results show that the proposed CHDA could detect communities with more precise recognition, regarding their characterization.
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Hsieh, TA., Li, KC., Huang, KC., Hsu, KH., Hsu, CH., Lai, KC. (2014). Community Identification in Multiple Relationship Social Networks. In: Park, J., Adeli, H., Park, N., Woungang, I. (eds) Mobile, Ubiquitous, and Intelligent Computing. Lecture Notes in Electrical Engineering, vol 274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40675-1_90
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DOI: https://doi.org/10.1007/978-3-642-40675-1_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40674-4
Online ISBN: 978-3-642-40675-1
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