Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142285
Title: Budget-efficient viral video distribution over online social networks : mining topic-aware influential users
Authors: Hu, Han
Wen, Yonggang
Feng, Shanshan
Keywords: Engineering::Computer science and engineering
Issue Date: 2016
Source: Hu, H., Wen, Y., & Feng, S. (2018). Budget-efficient viral video distribution over online social networks : mining topic-aware influential users. IEEE Transactions on Circuits and Systems for Video Technology, 28(3), 759-771. doi:10.1109/TCSVT.2016.2620152
Journal: IEEE Transactions on Circuits and Systems for Video Technology
Abstract: Marketing over online social networks (OSNs) has become an essential tool for spreading product information in a 'word of mouth' way. In particular, campaigns normally adopt a pragmatic approach of seeding videos with a selected list of influential users, hoping to create a viral distribution to reach as many users as possible. In this paper, we propose a multitopic-aware influence maximization framework to identify a fixed number of influential users and assign video clips of specific topics to them, with an ultimate objective to maximize the number of message deliveries, defined as expected posting number (EPN). We first prove the submodularity of the EPN function, resulting in a general greedy algorithm with a performance bound of 1-1/e. We further develop two faster algorithms to accelerate the computing speed for large-scale social networks. The first algorithm leverages two estimation methods to compute the upper bound for marginal EPN without a loss of accuracy. The second algorithm generates an approximation solution based on the upper bound and lower bound estimation, with a performance bound of ϵ(1-1/e). We have implemented a prototype system based on a private data center at the Nanyang Technological University campus in Singapore to enable video clip extraction and sharing among social users. Furthermore, we conduct experiments on four real large-scale social networks (with different scales and structures) and the results show that the proposed methods are much faster than previous algorithms but with high accuracy.
URI: https://hdl.handle.net/10356/142285
ISSN: 1051-8215
DOI: 10.1109/TCSVT.2016.2620152
Rights: © 2016 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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