Abstract
This poster paper presents an approach for tracking community structures. In contrast to the vast majority of existing methods, which are based on time-to-time consecutive evaluation, the proposed approach uses a similarity measure that involves the global temporal aspect of the network under investigation. A notable feature of our approach is that it is able to preserve the generated content across different time points. To demonstrate the suitability of the proposed method, we conducted experiments on real data extracted from the DBLP.
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© 2016 Springer International Publishing Switzerland
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Tajeuna, E.G., Bouguessa, M., Wang, S. (2016). Tracking Communities over Time in Dynamic Social Network. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_25
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DOI: https://doi.org/10.1007/978-3-319-41920-6_25
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41919-0
Online ISBN: 978-3-319-41920-6
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