An efficient evolutionary user interest community discovery model in dynamic social networks for internet of people
Journal article
Jiang, Liang, Shi, Leilei, Lu, Liu, Yao, Jingjing, Yuan, Bo and Zheng, Yongjun 2019. An efficient evolutionary user interest community discovery model in dynamic social networks for internet of people. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2019.2893625
| Authors | Jiang, Liang, Shi, Leilei, Lu, Liu, Yao, Jingjing, Yuan, Bo and Zheng, Yongjun |
|---|---|
| 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. |
| Keywords | Community discovery; Genetic algorithm; Internet of People; Label propagation; Nature-inspired algorithm |
| Year | 2019 |
| Journal | IEEE Internet of Things Journal |
| Publisher | IEEE |
| ISSN | 2327-4662 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/JIOT.2019.2893625 |
| Web address (URL) | http://hdl.handle.net/10545/623669 |
| http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
| hdl:10545/623669 | |
| Publication dates | 17 Jan 2019 |
| Publication process dates | |
| Deposited | 11 Apr 2019, 11:25 |
| Accepted | 15 Jan 2019 |
| Rights | Attribution-NonCommercial-NoDerivs 3.0 United States |
| Contributors | University of Derby |
| File | File Access Level Open |
| File | File Access Level Open |
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