An efficient evolutionary user interest community discovery model in dynamic social networks for internet of people
Abstract
Internet of People (IoP), which focuses on personal information collection by a wide range of the mobile applications, is the next frontier for Internet of Things (IoT). Nowadays, people become more and more dependent on the Internet, increasingly receiving and sending information on social networks (e.g., Twitter, etc.); thus social networks play a decisive role in IoP. Therefore, community discovery has emerged as one of the most challenging problems in social networks analysis. To this end, many algorithms have been proposed to detect communities in static networks. However, microblogging social networks are extremely dynamic in both content distribution and topological structure. In this paper, we propose a model EEUICD (Efficient Evolutionary User Interest Community Discovery) which employs a nature-inspired genetic algorithm to improve the quality of community discovery. Specifically, a preprocessing method based on HITS (Hypertext Induced Topic Search) improves the quality of initial users and posts, and a label propagation method is used to restrict the conditions of the mutation process to further improve the efficiency and effectiveness of user interest community detection. Finally, the experiments on the real datasets validate the effectiveness of the proposed model.Citation
Jiang, L., Shi, L., Liu, L., Yao, J., Yuan, B. and Zheng, Y., (2019). 'An Efficient Evolutionary User Interest Community Discovery Model in Dynamic Social Networks for Internet of People'. IEEE Internet of Things Journal. DOI: 10.1109/JIOT.2019.2893625Publisher
IEEEJournal
IEEE Internet of Things JournalDOI
10.1109/JIOT.2019.2893625Additional Links
https://ieeexplore.ieee.org/abstract/document/8616896Type
ArticleLanguage
enEISSN
2327-4662ae974a485f413a2113503eed53cd6c53
10.1109/JIOT.2019.2893625
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