Abstract
In the last decade social networks are becoming denser and denser, which makes analyzing their structures and properties very difficult. However, for certain task, if we can remove the inactive users and irrelevant links, the network will be amazingly sparse and tractable. In this paper we propose the Nonparametric Topic-aware Sparsification (NTAS) algorithm, which can simplify social networks for a specific task. To determine whether a link is relevant to the task, we adopt nonparametric topic model to analyze the topic distribution of links and the task. We empirically demonstrate that our algorithm can return a more sparse network compared with other state-of-the-art methods in the task of network monitoring.
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Acknowledgments
This work was supported by the Strategic Leading Science and Technology Projects of Chinese Academy of Sciences (No. XDA06030200) and 863 projects (No.2011AA01A103).
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Feng, W., Wang, P., Zhou, C., Hu, Y., Guo, L. (2015). Nonparametric Topic-Aware Sparsification of Influence Networks. In: Yueming, L., Xu, W., Xi, Z. (eds) Trustworthy Computing and Services. ISCTCS 2014. Communications in Computer and Information Science, vol 520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47401-3_11
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DOI: https://doi.org/10.1007/978-3-662-47401-3_11
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