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Community Detection and Profiling in Location-Based Social Networks

Community Detection and Profiling in Location-Based Social Networks
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Author(s): Zhu Wang (Northwestern Polytechnical University, China), Xingshe Zhou (Northwestern Polytechnical University, China), Daqing Zhang (Institut Telecom SudParis, France), Bin Guo (Northwestern Polytechnical University, China) and Zhiwen Yu (Northwestern Polytechnical University, China)
Copyright: 2014
Pages: 18
Source title: Creating Personal, Social, and Urban Awareness through Pervasive Computing
Source Author(s)/Editor(s): Bin Guo (Northwestern Polytechnical University, China), Daniele Riboni (University of Milano, Italy) and Peizhao Hu (NICTA, Australia)
DOI: 10.4018/978-1-4666-4695-7.ch007

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Abstract

Due to the proliferation of GPS-enabled smartphones, Location-Based Social Networking (LBSNs) services have been experiencing a remarkable growth over the last few years. Compared with traditional online social networks, a significant feature of LBSNs is the coexistence of both online and offline social interactions, providing a large-scale heterogeneous social network that is able to facilitate lots of academic studies. One possible study is to leverage both online and offline social ties for the recognition and profiling of community structures. In this chapter, the authors attempt to summarize some recent progress in the community detection problem based on LBSNs. In particular, starting with an empirical analysis on the characters of the LBSN data set, the authors present three different community detection approaches, namely, link-based community detection, content-based community detection, and hybrid community detection based on both links and contents. Meanwhile, they also address the community profiling problem, which is very useful in real-world applications.

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