Improving the influence under IC-N model in social networks
2015 ◽
Vol 07
(03)
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pp. 1550037
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Keyword(s):
The influence maximization problem in social networks is to find a set of seed nodes such that the total influence effect is maximized under certain cascade models. In this paper, we propose a novel task of improving influence, which is to find strategies to allocate the investment budget under IC-N model. We prove that our influence improving problem is 𝒩𝒫-hard, and propose new algorithms under IC-N model. To the best of our knowledge, our work is the first one that studies influence improving problem under bounded budget when negative opinions emerge. Finally, we implement extensive experiments over a large data collection obtained from real-world social networks, and evaluate the performance of our approach.
2018 ◽
Vol 26
(03)
◽
pp. 379-396
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2015 ◽
Vol 12
(4)
◽
pp. 48-62
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