Influence maximization over large-scale social networks: A bounded linear approach
Refereed conference paper presented and published in conference proceedings


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AbstractInformation diffusion in social networks is emerging as a promising solution to successful viral marketing, which relies on the effective and efficient identification of a set of nodes with the maximal social influence. While there are tremendous efforts on the development of social influence models and algorithms for social influence maximization, limited progress has been made in terms of designing both efficient and effective algorithms for finding a set of nodes with the maximal social influence. To this end, in this paper, we provide a bounded linear approach for influence computation and influence maximization. Specifically, we first adopt a linear and tractable approach to describe the influence propagation. Then, we develop a quantitative metric, named Group-PageRank, to quickly estimate the upper bound of the social influence based on this linear approach. More importantly, we provide two algorithms Linear and Bound, which exploit the linear approach and Group-PageRank for social influence maximization. Finally, extensive experimental results demonstrate that (a) the adopted linear approach has a close relationship with traditional models and Group-PageRank provides a good estimation of social influence; (b) Linear and Bound can quickly find a set of the most influential nodes and both of them are scalable for large-scale social networks.
All Author(s) ListLiu Q., Xiang B., Chen E., Xiong H., Tang F., Yu J.X.
Name of Conference23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
Start Date of Conference03/11/2014
End Date of Conference07/11/2014
Place of ConferenceShanghai
Country/Region of ConferenceChina
Detailed descriptionorganized by CIKM'14,
Year2014
Month1
Day1
Pages171 - 180
ISBN9781450325981
LanguagesEnglish-United Kingdom
KeywordsBound, Linear approach, Social influence, Viral marketing

Last updated on 2021-21-02 at 00:37