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. Attached Files Name Description MIMEType Size Downloads Title Mining mechanism of top-k influential nodes based on voting algorithm in mobile social networks Author(s) Peng,S Wang,G 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: Faculty of Science, Engineering and Built Environment School of Information Technology 2018 ERA Submission Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 22 Apr 2015, 14:31:03 EST Wed, 22 Apr 2015, 14:31:39 EST Wed, 22 Apr 2015, 15:55:58 EST Thu, 23 Apr 2015, 05:00:48 EST Thu, 23 Apr 2015, 06:08:29 EST Mon, 04 May 2015, 11:22:24 EST Mon, 18 May 2015, 14:16:40 EST Mon, 18 May 2015, 14:17:35 EST Wed, 14 Oct 2015, 14:22:59 EST Filtered Full Citation counts:   Cited 4 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 251 Abstract Views, 11 File Downloads  -  Detailed Statistics Created: Wed, 22 Apr 2015, 15:55:58 EST

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