Abstract
The community detection is considered as one of the most important tools to discover useful information in large scale networks, which is difficult to obtain by simple observations. A lot of research work has been done in the past. In addition, the finding of the most influential users in the network is also a challenging task. The current state of the art algorithms in community detection demonstrated their effectiveness on a variety of networks, most of them, however, suffer from scalability issues and few of them are largely dependent on the network topology. To address this problem, we propose a dynamic community structure method, for the detection of a community in large scale networks. In our proposed method, the community structure of a network is improved by finding the most influential community nodes over time. The proposed method overcomes the deficiencies of prior similar community detection methods. The experimental results proved the efficiency of our method over three states of the art algorithms on both synthetic benchmark networks and real-world networks.
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Acknowledgments
This research was supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program. No. 10063130, Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1A2C1006159), and MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2016-0-00313) supervised by the IITP (Institute for Information & communications Technology Promotion).
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Amin, F., Choi, JG., Choi, G.S. (2020). Community Detection Based on Social Influence in Large Scale Networks. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing, vol 1150. Springer, Cham. https://doi.org/10.1007/978-3-030-44038-1_12
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DOI: https://doi.org/10.1007/978-3-030-44038-1_12
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