Mining mechanism of top-k influential nodes based on voting algorithm in mobile social networks

Peng,S, Wang,G and Yu,S 2013, Mining mechanism of top-k influential nodes based on voting algorithm in mobile social networks, in Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013, IEEE Computer Society,, pp. 2194-2199, doi: 10.1109/HPCC.and.EUC.2013.314.

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Title Mining mechanism of top-k influential nodes based on voting algorithm in mobile social networks
Author(s) Peng,S
Wang,G
Yu,SORCID iD for Yu,S orcid.org/0000-0003-4485-6743
Conference location Zhangjiajie, China
Conference dates 2013/11/13 - 2013/11/15
Title of proceedings Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013
Publication date 2013
Start page 2194
End page 2199
Publisher IEEE Computer Society
Keyword(s) election mechanism
intimacy degree
mobile social networks
social relationship graph
top-k
Summary In recent years, evaluating the influence of nodes and finding top-k influential nodes in social networks, has drawn a wide attention and has become a hot-pot research issue. Considering the characteristics of social networks, we present a novel mechanism to mine the top-k influential nodes in mobile social networks. The proposed mechanism is based on the behaviors analysis of SMS/MMS (simple messaging service / multimedia messaging service) communication between mobile users. We introduce the complex network theory to build a social relation graph, which is used to reveal the relationship among people's social contacts and messages sending. Moreover, intimacy degree is also introduced to characterize social frequency among nodes. Election mechanism is hired to find the most influential node, and then a heap sorting algorithm is used to sort the voting results to find the k most influential nodes. The experimental results show that the mechanism can finds out the most influential top-k nodes efficiently and effectively. © 2013 IEEE.
ISBN 9780769550886
Language eng
DOI 10.1109/HPCC.and.EUC.2013.314
Field of Research 080503 Networking and Communications
Socio Economic Objective 890101 Fixed Line Data Networks and Services
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072605

Document type: Conference Paper
Collections: School of Information Technology
2018 ERA Submission
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